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NEUDM

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NEU-Datamining 20

generation

absa-quad

['Never have I had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b/c I live in a walkup ) who always seem disappointed with their tips .']

[['delivery guys', 'service general', 'negative', 'dramatic']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .']

[['staff', 'service general', 'negative', 'good'], ['Cafe Noir', 'restaurant general', 'positive', 'great']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['I stumbled upon this great pizzeria as I explored my new neighborhood .']

[['pizzeria', 'restaurant general', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Prices too high for this cramped and unappealing resturant .']

[['resturant', 'restaurant prices', 'negative', 'high'], ['resturant', 'ambience general', 'negative', 'cramped'], ['resturant', 'ambience general', 'negative', 'unappealing']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Everything is always cooked to perfection , the service is excellent , the decor cool and understated .']

[['NULL', 'food quality', 'positive', 'perfection'], ['service', 'service general', 'positive', 'excellent'], ['decor', 'ambience general', 'positive', 'cool'], ['decor', 'ambience general', 'positive', 'understated']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Everything about this restaurant was special .']

[['restaurant', 'restaurant general', 'positive', 'special']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Warm and friendly in the winter and terrific outdoor seating in the warmer months .']

[['NULL', 'ambience general', 'positive', 'Warm'], ['NULL', 'ambience general', 'positive', 'friendly'], ['outdoor seating', 'ambience general', 'positive', 'terrific']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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["I have been to Roth 's twice and both times were very disappointing ."]

[["Roth 's", 'restaurant general', 'negative', 'disappointing']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Best Pastrami I ever had and great portion without being ridiculous .']

[['Pastrami', 'food quality', 'positive', 'Best'], ['portion', 'food style_options', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['Service here was great , food was fantastic .']

[['Service', 'service general', 'positive', 'great'], ['food', 'food quality', 'positive', 'fantastic']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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["If celebrities make you sweat , then your in for a ride , but if your like most around these parts then you 'll just yawn and wonder whats with all the hype ."]

[['NULL', 'restaurant miscellaneous', 'negative', 'yawn']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .']

[['NULL', 'ambience general', 'neutral', 'packed'], ['vibe', 'ambience general', 'positive', 'good'], ['French food', 'food quality', 'positive', 'best']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['love the food .']

[['food', 'food quality', 'positive', 'love']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

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['The combination of super-fresh ingredients in the dishes are unusual but really delicious .']

[['ingredients', 'food quality', 'positive', 'super-fresh'], ['ingredients', 'food quality', 'positive', 'delicious']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .']

[['cart attendant', 'service general', 'negative', 'walked away']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I pray it stays open forever .']

[['NULL', 'restaurant general', 'positive', 'stays open forever']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Service was slow , but the people were friendly .']

[['Service', 'service general', 'negative', 'slow'], ['people', 'service general', 'positive', 'friendly']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Lucky Strike is a great casual place to just grab a bite to eat .']

[['Lucky Strike', 'restaurant general', 'positive', 'great casual']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines ."]

[['food', 'food quality', 'positive', 'amazing'], ['selection of wines', 'drinks style_options', 'positive', 'extensive'], ["Chef 's tasting menu", 'food quality', 'positive', 'favourite']]

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Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Baluchi 's has solid food and a nice decor at reasonable prices ."]

[['food', 'food quality', 'positive', 'solid'], ['decor', 'ambience general', 'positive', 'nice'], ["Baluchi 's", 'restaurant prices', 'positive', 'solid']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['As many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .']

[['NULL', 'service general', 'neutral', 'slow']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .']

[['service', 'service general', 'positive', 'impeccable'], ['service', 'service general', 'positive', 'unobtrusive']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["I have been to Rao 's probably 15 times the past 3 years and it keeps getting better ."]

[["Rao 's", 'restaurant general', 'positive', 'better']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .']

[['Pizza', 'food quality', 'positive', 'crave']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .']

[['atmosphere', 'ambience general', 'positive', 'nothing special']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['IT WAS HORRIBLE .']

[['NULL', 'restaurant general', 'negative', 'HORRIBLE']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['But , they were too big for the bun .']

[['NULL', 'food style_options', 'negative', 'too big']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Thanks Bloom 's for a lovely trip ."]

[["Bloom 's", 'restaurant general', 'positive', 'Thanks'], ["Bloom 's", 'restaurant general', 'positive', 'lovely']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I love it .']

[['NULL', 'restaurant general', 'positive', 'love']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Spreads and toppings are great - though a bit pricey .']

[['Spreads', 'food quality', 'positive', 'great'], ['toppings', 'food quality', 'positive', 'great'], ['Spreads', 'food prices', 'negative', 'pricey'], ['toppings', 'food prices', 'negative', 'pricey']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["The food 's as good as ever ."]

[['food', 'food quality', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Decor is charming .']

[['Decor', 'ambience general', 'positive', 'charming']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food was good .']

[['food', 'food quality', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Delicate spices , onions , eggs and a kick-ass roti .']

[['spices', 'food quality', 'positive', 'Delicate'], ['onions', 'food quality', 'positive', 'Delicate'], ['eggs', 'food quality', 'positive', 'Delicate'], ['roti', 'food quality', 'positive', 'kick-ass']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["The pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before ."]

[['pesto pizza', 'food quality', 'positive', 'excellent'], ['spicy Italian cheese', 'food quality', 'positive', 'nice']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Wonderful at holiday time .']

[['NULL', 'restaurant miscellaneous', 'positive', 'Wonderful']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food is great and they have a good selection of wines at reasonable prices .']

[['food', 'food quality', 'positive', 'great'], ['wines', 'drinks style_options', 'positive', 'good selection'], ['wines', 'drinks prices', 'positive', 'good selection']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .']

[['meals', 'food quality', 'negative', 'edible'], ['rosemary or orange flavoring', 'food quality', 'negative', 'weird']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I would defiantly come back here again as one of my top choices .']

[['NULL', 'restaurant general', 'positive', 'top choices']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I have lived in Japan for 7 years and the taste of the food and the feel of the restaurant is like being back in Japan .']

[['food', 'food quality', 'positive', 'like being back in Japan'], ['feel', 'ambience general', 'positive', 'like being back in Japan']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I found the food , service and value exceptional everytime I have been there .']

[['food', 'food quality', 'positive', 'exceptional'], ['service', 'service general', 'positive', 'exceptional'], ['value', 'restaurant prices', 'positive', 'exceptional']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The rice was poor quality and was cooked so badly it was hard .']

[['rice', 'food quality', 'negative', 'poor quality'], ['rice', 'food quality', 'negative', 'cooked so badly'], ['rice', 'food quality', 'negative', 'hard']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food now is inconsistent .']

[['food', 'food quality', 'negative', 'inconsistent']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Cute and decorative .']

[['NULL', 'ambience general', 'positive', 'Cute'], ['NULL', 'ambience general', 'positive', 'decorative']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['This place is always very crowded and popular .']

[['place', 'restaurant miscellaneous', 'positive', 'crowded'], ['place', 'restaurant miscellaneous', 'positive', 'popular']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside ."]

[['main dining room', 'ambience general', 'positive', 'spectacular'], ['ceiling', 'ambience general', 'positive', 'spectacular'], ['ceiling', 'ambience general', 'positive', 'hand-painted high']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['sometimes i get bad food and bad service , sometimes i get good good and bad service .']

[['food', 'food quality', 'negative', 'bad'], ['service', 'service general', 'negative', 'bad'], ['service', 'service general', 'negative', 'bad'], ['good', 'food quality', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['There is a lot of variety even for people who eat vegetarian like me .']

[['NULL', 'food style_options', 'positive', 'a lot of variety']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['whoever the jazz duo was , they were on POINT .']

[['jazz duo', 'ambience general', 'positive', 'on POINT']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Most importantly , food is excellent .']

[['food', 'food quality', 'positive', 'excellent']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I was pleasantly surprised to find this gem in Hoboken .']

[['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['noodles with shrimp and chicken and coconut juice is the MUST !']

[['noodles with shrimp and chicken and coconut juice', 'food quality', 'positive', 'MUST']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['However , I think this place is a good hang out spot .']

[['place', 'ambience general', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !']

[['spot', 'restaurant miscellaneous', 'positive', 'perfect']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['It is expensive but well worth the money .']

[['NULL', 'restaurant prices', 'negative', 'expensive'], ['NULL', 'restaurant general', 'positive', 'well worth']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Cozy romantic atomosphere with only around 15 tables at most .']

[['atomosphere', 'ambience general', 'positive', 'Cozy romantic']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["It 's a rather cramped and busy restaurant and it closes early ."]

[['restaurant', 'restaurant miscellaneous', 'negative', 'closes early'], ['restaurant', 'ambience general', 'negative', 'cramped'], ['restaurant', 'ambience general', 'negative', 'busy']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Wine list is extensive without being over-priced .']

[['Wine list', 'drinks style_options', 'positive', 'extensive without being over-priced'], ['Wine list', 'drinks prices', 'positive', 'extensive without being over-priced']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["And even with it 's Pub atmosphere they were great to my kids too !"]

[['NULL', 'service general', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I have been doing all of the above at the Heartland Brewery for over 5 years now and I HAVE NEVER BEEN DISAPPOINTED !']

[['Heartland Brewery', 'restaurant general', 'positive', 'NEVER BEEN DISAPPOINTED']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Right off the L in Brooklyn this is a nice cozy place with good pizza .']

[['pizza', 'food quality', 'positive', 'good'], ['place', 'ambience general', 'positive', 'nice cozy']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food here does a great service to the name ( Cantonese that is ... ) .']

[['food', 'food quality', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .']

[['Myagi', 'restaurant general', 'positive', 'favorite']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Wouldn 't recomend it for dinner !"]

[['NULL', 'restaurant general', 'negative', "Wouldn 't recomend"]]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I ordered the smoked salmon and roe appetizer and it was off flavor .']

[['smoked salmon and roe appetizer', 'food quality', 'negative', 'off flavor']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food was average or above including some surprising tasty dishes .']

[['food', 'food quality', 'positive', 'average or above'], ['dishes', 'food quality', 'positive', 'tasty']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['And really large portions .']

[['portions', 'food style_options', 'positive', 'large']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Service , however , was excellent ... and I liked the setting/atmosphere a lot .']

[['Service', 'service general', 'positive', 'excellent'], ['setting/atmosphere', 'ambience general', 'positive', 'liked']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I highly recommend it .']

[['NULL', 'restaurant general', 'positive', 'recommend']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['fine dining restaurant quality .']

[['dining', 'food quality', 'positive', 'fine']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Nice atmosphere , the service was very pleasant and the desert was good .']

[['atmosphere', 'ambience general', 'positive', 'Nice'], ['service', 'service general', 'positive', 'pleasant'], ['desert', 'food quality', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['LOVE the atmosphere - felt like I was in Paris .']

[['atmosphere', 'ambience general', 'positive', 'LOVE']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['I would highly recommand requesting a table by the window .']

[['table by the window', 'location general', 'positive', 'recommand']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .']

[['sushi', 'food quality', 'positive', 'great'], ['thai food', 'food quality', 'positive', 'excellent'], ['Planet Thailand', 'restaurant general', 'positive', 'hit']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The flavors robust and subtle .']

[['NULL', 'food quality', 'positive', 'robust'], ['NULL', 'food quality', 'positive', 'subtle']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...']

[['NULL', 'restaurant prices', 'neutral', 'quibbles'], ['measures of liquers', 'drinks style_options', 'positive', 'pour-your-own'], ['measures of liquers', 'drinks style_options', 'positive', 'courtesey']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The food was mediocre at best but it was the horrible service that made me vow never to go back .']

[['food', 'food quality', 'negative', 'mediocre'], ['service', 'service general', 'negative', 'horrible']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Such a disappointment ...']

[['NULL', 'service general', 'negative', 'disappointment']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Our food was great too !']

[['food', 'food quality', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Overall , not worth the money .']

[['NULL', 'restaurant prices', 'negative', 'not worth']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['This place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .']

[['place', 'restaurant prices', 'negative', 'pricey'], ['food', 'food quality', 'positive', 'worth'], ['service', 'service general', 'negative', 'paying a quater of the price']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The mussels were fantastic and so was the dessert ... definitely going to be back very soon .']

[['mussels', 'food quality', 'positive', 'fantastic'], ['dessert', 'food quality', 'positive', 'fantastic'], ['NULL', 'restaurant general', 'positive', 'going to be back']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["if you 're daring , try the balsamic vinegar over icecream , it 's wonderful !"]

[['balsamic vinegar over icecream', 'food quality', 'positive', 'try'], ['balsamic vinegar over icecream', 'food quality', 'positive', 'wonderful']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Overall , excellent restaurant !']

[['restaurant', 'restaurant general', 'positive', 'excellent']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['My wife and I ate here earlier this week and have not stopped ranting and raving about the food .']

[['food', 'food quality', 'positive', 'raving']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['A little crowded but they move that line really fast !']

[['NULL', 'service general', 'positive', 'fast'], ['NULL', 'restaurant miscellaneous', 'negative', 'crowded']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Go there once and oh yes ... you will go back ... you will ...']

[['NULL', 'restaurant general', 'positive', 'go back']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['it is not consistent .']

[['NULL', 'restaurant general', 'negative', 'not consistent']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["The pizza was delivered cold and the cheese was n't even fully melted !"]

[['pizza', 'food quality', 'negative', 'cold'], ['cheese', 'food quality', 'negative', "was n't even fully melted"]]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .']

[['staff', 'service general', 'positive', 'nice'], ['staff', 'service general', 'negative', 'stressed'], ['unisex bathroom', 'ambience general', 'negative', 'needs to be cleaned']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Unique apppetizers .']

[['apppetizers', 'food quality', 'positive', 'Unique']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .']

[['restaurant', 'restaurant general', 'positive', 'great']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["The service is ok , some of the people did n't get what they asked for ."]

[['service', 'service general', 'neutral', 'ok']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['Simply some good tasting Chinese food at incredible prices ...']

[['Chinese food', 'food quality', 'positive', 'good tasting'], ['Chinese food', 'food prices', 'positive', 'good tasting']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["I 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square ."]

[['Cafe Spice', 'restaurant general', 'positive', 'best']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['La Rosa waltzes in , and I think they are doing it the best .']

[['La Rosa', 'food quality', 'positive', 'best']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['The design and atmosphere is just as good .']

[['design', 'ambience general', 'positive', 'good'], ['atmosphere', 'ambience general', 'positive', 'good']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

["Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor ."]

[['host', 'service general', 'positive', 'amazing'], ['crew', 'service general', 'positive', 'treated me as family']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['But the pizza is way to expensive .']

[['pizza', 'food prices', 'negative', 'expensive']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'

generation

absa-quad

['While their kitchen food is delicious , their Sushi is out of this world .']

[['kitchen food', 'food quality', 'positive', 'delicious'], ['Sushi', 'food quality', 'positive', 'out of this world']]

none

Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example: Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty" Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'