QA-for-Event-Extraction

A question answering method for Event Extraction on your own Chinese dataset (Also in English dataset).

Prerequisites

Install the packages.
```
pip install pytorch_pretrained_bert
pip install numpy torch
```
Download BERT-base-chinese model from https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz

Download bert-base-chinese-vocab from https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt.

Then put these file in
```
./bert-base-chinese
   --bert_config.json
   --pytorch_model.bin
   --vocab.txt
```

Prepare Your own dataset.

sample.json

[
  {
    "words": "在冲突中，一名年轻的激进示威者用尖刀刺伤警员。",
    "golden-event-mentions": [
      {
        "event_type": "冲突：冲击",
        "trigger": {
          "text": "刺伤",
          "start": 18,
          "end": 20
        },
        "arguments": [
          {
            "text": "警员",
            "start": 20,
            "end": 22,
            "entity_type": "人物",
            "role": "受害者"
          },
          {
            "text": "激进示威者",
            "start": 10,
            "end": 15,
            "entity_type": "人物",
            "role": "攻击者"
          }
        ]
      }
    ]
  },
]

put the data into

./data/
   --train.json
   --dev.json
   --test.json

Add your trigger and argument category in the file ./const.py

Design question templates based on your event trigger and argument in ./question.csv

for example

   ##事件类型,事件角色,设计的问题##
   冲突：冲击,攻击者,谁发起了这次攻击事件？

Usage

Run:

sudo python qa-trigger.py

You can get Precision/Recall/F1 on event trigger extraction.
Then you can get the "trigger_model.pt" in main directory.

sudo python qa-argument.py

You can get Precision/Recall/F1 on event argument extraction.
Then you can get the "argument_model.pt" in main directory.

Reference

xinyadu's eeqa repository [github]
Nlpcl-lab's bert_event_extraction repository [github]

kevinuserdd/QA-for-Event-Extraction

QA-for-Event-Extraction

Prerequisites

Usage

Reference