# Attention-CNN-relation-extraction

**Repository Path**: zenglanllan/Attention-CNN-relation-extraction

## Basic Information

- **Project Name**: Attention-CNN-relation-extraction
- **Description**: Attention-CNN-relation-extraction
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-07-27
- **Last Updated**: 2023-02-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Attention-CNN-relation-extraction
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-based-convolutional-neural-network-2/relation-extraction-on-semeval-2010-task-8)](https://paperswithcode.com/sota/relation-extraction-on-semeval-2010-task-8?p=attention-based-convolutional-neural-network-2)

Implementation of [Attention-Based Convolutional Neural Network for Semantic Relation Extraction](https://www.aclweb.org/anthology/C16-1238.pdf).

## Environment Requirements
* python 3.6
* pytorch 1.3.0

## Data
* [SemEval2010 Task8](https://drive.google.com/file/d/0B_jQiLugGTAkMDQ5ZjZiMTUtMzQ1Yy00YWNmLWJlZDYtOWY1ZDMwY2U4YjFk/view?sort=name&layout=list&num=50) \[[paper](https://www.aclweb.org/anthology/S10-1006.pdf)\]
* [Google News - Mikolov et
al.(2010)](https://code.google.com/archive/p/word2vec/) \[[paper](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)\]

## Usage
1. Download the embedding in the `embedding` folder and use `convert.py` to convert it to the `UTF-8` format.
2. Run the following the commands to start the program.
```shell
python run.py
```
More details can be seen by `python run.py -h`.

3. You can use the official scorer to check the final predicted result.
```shell
perl semeval2010_task8_scorer-v1.2.pl proposed_answer.txt predicted_result.txt >> result.txt
```

## Result
The result of my version and that in paper are present as follows:
| paper | my version |
| :------: | :------: |
| 0.843 | 0.8156 |

The training log can be seen in `train.log` and the official evaluation results is available in `result.txt`.

*Note*:
* Some settings are different from those mentioned in the paper.
* No validation set used during training.
* Just complete the part of general *Attention-CNN*. WordNet and words around nominals are not used. More details are available in Section 4 in this paper.
* Although I try to set random seeds, it seems that the results of each run are a little different.
* The result of my version is not ideal. Maybe my understanding is wrong. If you find it, please let me know.

## Reference Link