Update README_EN.md

README_EN.md

@@ -22,31 +22,44 @@ zjunlp/knowlm-13b-ie samples around 10% of the data from Chinese-English informa
 
 
 # 2. Information Extraction Template
-The template `template` is used to construct the instruction `instruction` for input to the model. It consists of three parts:
-1. Task description
-2. List of candidate labels {s_schema} (optional)
-3. Structural output format {s_format}
+The `template` is used to construct an `instruction` for the input of model, consisting of three parts:
+1. **Task Description**: Clearly define the model's function and the task it needs to complete, such as entity recognition, relation extraction, event extraction, etc.
+2. **Candidate Label List {s_schema} (optional)**: Define the categories of labels that the model needs to extract, such as entity types, relation types, event types, etc.
+3. **Structured Output Format {s_format}**: Specify how the model should present the structured information it extracts.
 
 
-Template with specified list of candidate labels:
-```json
-    NER: "You are an expert specialized in entity extraction. With the candidate entity types list: {s_schema}, please extract possible entities from the input below, outputting NAN if a certain entity does not exist. Respond in the format {s_format}."
-    RE: "You are an expert in extracting relation triples. With the candidate relation list: {s_schema}, please extract the possible head entities and tail entities from the input below and provide the corresponding relation triples. If a relation does not exist, output NAN. Please answer in the {s_format} format."
-    EE: "You are a specialist in event extraction. Given the candidate event dictionary: {s_schema}, please extract any possible events from the input below. If an event does not exist, output NAN. Please answer in the format of {s_format}."
-    EET: "As an event analysis specialist, you need to review the input and determine possible events based on the event type directory: {s_schema}. All answers should be based on the {s_format} format. If the event type does not match, please mark with NAN."
-    EEA: "You are an expert in event argument extraction. Given the event dictionary: {s_schema1}, and the event type and trigger words: {s_schema2}, please extract possible arguments from the following input. If an event argument does not exist, output NAN. Please respond in the {s_format} format."
+Template **with specified list of candidate labels**:
 ```
+Named Entity Recognition(NER): You are an expert specialized in entity extraction. With the candidate entity types list: {s_schema}, please extract possible entities from the input below, outputting NAN if a certain entity does not exist. Respond in the format {s_format}.
 
+Relation Extraction(RE): You are an expert in extracting relation triples. With the candidate relation list: {s_schema}, please extract the possible head entities and tail entities from the input below and provide the corresponding relation triples. If a relation does not exist, output NAN. Please answer in the {s_format} format.
 
-Template without specifying a list of candidate labels:
-```json
-    NER: "Analyze the text content and extract the clear entities. Present your findings in the {s_format} format, skipping any ambiguous or uncertain parts."
-    RE: "Please extract all the relation triples from the text and present the results in the format of {s_format}. Ignore those entities that do not conform to the standard relation template."
-    EE: "Please analyze the following text, extract all identifiable events, and present them in the specified format {s_format}. If certain information does not constitute an event, simply skip it."
-    EET: "Examine the following text content and extract any events you deem significant. Provide your findings in the {s_format} format."
-    EEA: "Please extract possible arguments based on the event type and trigger word {s_schema2} from the input below. Answer in the format of {s_format}."
+Event Extraction(EE): You are a specialist in event extraction. Given the candidate event dictionary: {s_schema}, please extract any possible events from the input below. If an event does not exist, output NAN. Please answer in the format of {s_format}.
+
+Event Type Extraction(EET): As an event analysis specialist, you need to review the input and determine possible events based on the event type directory: {s_schema}. All answers should be based on the {s_format} format. If the event type does not match, please mark with NAN.
+
+Event Argument Extraction(EEA): You are an expert in event argument extraction. Given the event dictionary: {s_schema1}, and the event type and trigger words: {s_schema2}, please extract possible arguments from the following input. If an event argument does not exist, output NAN. Please respond in the {s_format} format.
 ```
 
+
+<details>
+    <summary><b>Template without specifying a list of candidate labels</b></summary>
+
+
+  ```
+  Named Entity Recognition(NER): Analyze the text content and extract the clear entities. Present your findings in the {s_format} format, skipping any ambiguous or uncertain parts.
+
+  Relation Extraction(RE): Please extract all the relation triples from the text and present the results in the format of {s_format}. Ignore those entities that do not conform to the standard relation template.
+
+  Event Extraction(EE): Please analyze the following text, extract all identifiable events, and present them in the specified format {s_format}. If certain information does not constitute an event, simply skip it.
+
+  Event Type Extraction(EET): Examine the following text content and extract any events you deem significant. Provide your findings in the {s_format} format.
+
+  Event Argument Extraction(EEA): Please extract possible arguments based on the event type and trigger word {s_schema2} from the input below. Answer in the format of {s_format}.
+  ```
+</details>
+
+
 <details>
     <summary><b>Candidate Labels {s_schema}</b></summary>
 
@@ -67,11 +80,15 @@ Template without specifying a list of candidate labels:
 
 
     ```json
-        NER: (Entity,Entity Type)
-        RE: (Subject,Relation,Object)
-        EE: (Event Trigger,Event Type,Argument1#Argument Role1;Argument2#Argument Role2)
-        EET: (Event Trigger,Event Type)
-        EEA: (Event Trigger,Event Type,Argument1#Argument Role1;Argument2#Argument Role2)
+        Named Entity Recognition(NER): (Entity,Entity Type)
+
+        Relation Extraction(RE): (Subject,Relation,Object)
+    
+        Event Extraction(EE): (Event Trigger,Event Type,Argument1#Argument Role1;Argument2#Argument Role2)
+    
+        Event Type Extraction(EET): (Event Trigger,Event Type)
+    
+        Event Argument Extraction(EEA): (Event Trigger,Event Type,Argument1#Argument Role1;Argument2#Argument Role2)
     ```
 
 </details>
@@ -229,13 +246,13 @@ For Event Extraction (EE) tasks:
 {"OrganizationalRelation-Layoff": ["LayoffParty", "NumberLaidOff", "Time"], "LegalAction-Sue": ["Plaintiff", "Defendant", "Time"], ...}         # Dictionary of event types
 
 
-For EET tasks:
+For Event Type Extraction(EET) tasks:
 ["Social Interaction-Thanks", "Organizational Action-OpeningCeremony", "Competition Action-Withdrawal", ...]         # List of event types
 []                               # Empty list
 {}                               # Empty dictionary
 
 
-For Event Extraction with Arguments (EEA) tasks:
+For Event Argument Extraction(EEA) tasks:
 ["Social Interaction-Thanks", "Organizational Action-OpeningCeremony", "Competition Action-Withdrawal", ...]                  # List of event types
 ["DismissingParty", "TerminatingParty", "Reporter", "ArrestedPerson"]           # List of argument roles
 {"OrganizationalRelation-Layoff": ["LayoffParty", "NumberLaidOff", "Time"], "LegalAction-Sue": ["Plaintiff", "Defendant", "Time"], ...}             # Dictionary of event types