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https://github.com/TanyaZhao/MRC4ERE_plus
Implementation for Paper "Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction"
https://github.com/TanyaZhao/MRC4ERE_plus
Last synced: 3 months ago
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Implementation for Paper "Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction"
- Host: GitHub
- URL: https://github.com/TanyaZhao/MRC4ERE_plus
- Owner: TanyaZhao
- Created: 2020-04-23T08:35:32.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-10-10T08:03:17.000Z (about 4 years ago)
- Last Synced: 2024-06-24T05:45:39.924Z (5 months ago)
- Language: Python
- Size: 1.09 MB
- Stars: 38
- Watchers: 2
- Forks: 5
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - TanyaZhao/MRC4ERE_plus
README
# MRC4ERE++
The repository contains the code for Paper "Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction", accepted by IJCAI 2020 (https://www.ijcai.org/Proceedings/2020/0546.pdf).
If you find this repo helpful, please cite the following:
```text
@inproceedings{zhao-etal-2020-asking,
title = "Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction",
author = "Zhao, Tianyang and
Yan, Zhao and
Cao, Yunbo and
Li, Zhoujun",
booktitle = "Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence",
month = jan,
year = "2021",
address = "Kyoto, Japan",
publisher = "International Joint Conferences on Artificial Intelligence",
url = "https://www.ijcai.org/Proceedings/2020/0546.pdf",
pages = "3948--3954"
}
```
## Overview
In this paper, we improve the existing MRCbased entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning.
For example, when extracting a person entity, we can construct diverse questions as follows:
- Who is mentioned in the context?
- Find people mentioned in the context?
- Which words are person entities?
After extracted the head entities, we generate diverse questions to identify tail entities by querying about protential relations.
For example, given the person ```Paul Vercammen``` and the relation ```Lived_In```, questions can be constructed as:
- Find locations which Paul Vercammen is lived in ?
- Where does Paul Vercammen live ?
- Where is Paul Vercammen's home ?
## Contents
1. [Experimental Results](#experimental-results)
2. [Dependencies](#dependencies)
3. [Usage](#usage)
## Experimental Results
We evaluate the proposed method on two widely-used datasets for entity relation extaction: ACE05 and CoNLL04.
Micro precision, recall and F1-score are used as evaluation metrics.
- Results on **ACE 2005**:
| *Models* | Enity P | Entity R | Entity F | Relation P | Relation R | Relation F|
| --- | --- | --- | --- | --- | --- | --- |
|Sun et al. (2018) |83.9 s|83.2| 83.6| 64.9| 55.1| 59.6|
|Li et al. (2019) |84.7 |84.9| 84.8 |64.8| 56.2| 60.2 |
|MRC4ERE++ |85.9 |85.2 |**85.5** |62.0| 62.2| **62.1**|
- Results on **CoNLL 2004**:
| *Models* | Enity P | Entity R | Entity F | Relation P | Relation R | Relation F|
| --- | --- | --- | --- | --- | --- | --- |
|Zhang et al. (2017) |– |–| 85.6 |– |–| 67.8|
|Li et al. (2019) | 89.0 | 86.6 | 87.8 | 69.2 | 68.2 | 68.9 |
|MRC4ERE++ |89.3 |88.5|**88.9** |72.2| 71.5| **71.9**|
## Data Preparation
We take the CoNLL04 dataset as an example:
* We have processed the [original data](https://github.com/bekou/multihead_joint_entity_relation_extraction/tree/master/data/CoNLL04) into the MRC-based formation, as listed in the directory ```datasets/conll04/mrc4ere```.
To use the pretrained language model BERT:
* Download [BERT-Base-Cased, English](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz) pretrained model and unzip it into the directory ```pretrained_bert/bert-base-cased/```. In this way, we can load the BERT from local working directory.
## Dependencies
* Package dependencies:
```bash
python >= 3.6
PyTorch == 1.1.0
pytorch-pretrained-bert == 0.6.1
```
## Usage
As an example, the following command trains the proposed mothod on CoNLL04.
```bash
cd run
python run_tagger.py
```