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https://github.com/czhang99/SynonymNet

Entity Synonym Discovery via Multipiece Bilateral Context Matching (IJCAI'20) https://arxiv.org/abs/1901.00056
https://github.com/czhang99/SynonymNet

deep-learning ijcai2020 synonym-detection synonym-discovery synonym-matching

Last synced: about 1 month ago
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Entity Synonym Discovery via Multipiece Bilateral Context Matching (IJCAI'20) https://arxiv.org/abs/1901.00056

Host: GitHub
URL: https://github.com/czhang99/SynonymNet
Owner: czhang99
Created: 2020-05-11T00:35:22.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2023-03-24T22:22:27.000Z (over 1 year ago)
Last Synced: 2024-08-03T09:07:27.526Z (5 months ago)
Topics: deep-learning, ijcai2020, synonym-detection, synonym-discovery, synonym-matching
Language: Python
Size: 14.6 KB
Stars: 30
Watchers: 3
Forks: 7
Open Issues: 6
Metadata Files:
- Readme: README.md

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README

# Entity Synonym Discovery via Multipiece Bilateral Context Matching

This project provides source code and data for SynonymNet, a model that detects entity synonyms via multipiece bilateral context matching.

Details about SynonymNet can be accessed [here](https://arxiv.org/abs/1901.00056), and the implementation is based on the Tensorflow library.

## Quick Links
- [Installation](#installation)
- [Usage](#usage)
- [Data](#data)
- [Results](#results)
- [Acknowledgements](#acknowledgements)

## Installation

For training, a GPU is recommended to accelerate the training speed.

### Tensorflow

The code is based on Tensorflow 1.5 and can run on Tensorflow 1.15.0. You can find installation instructions [here](https://www.tensorflow.org/install).

### Dependencies

The code is written in Python 3.7. Its dependencies are summarized in the file ```requirements.txt```.

tensorflow_gpu==1.15.0

numpy==1.14.0

pandas==0.25.1

gensim==3.8.1

scikit_learn==0.21.2

You can install these dependencies like this:
```
pip3 install -r requirements.txt
```
## Usage
* Run the model on Wikipedia+Freebase dataset with the siamese architecture and the default hyperparameter settings

```cd src```

```python3 train_siamese.py --dataset=wiki```

* For all available hyperparameter settings, use

```python3 train_siamese.py -h```

* Run the model on Wikipedia+Freebase dataset with the triplet architecture and the default hyperparameter settings

```cd src```

```python3 train_triplet.py --dataset=wiki```

## Data
### Format
Data
Each dataset is a folder under the ```./input_data``` folder, where each sub-folder indicates a train/val/test split:
```
./data
└── wiki
├── train
|   ├── siamese_contexts.txt
|   └── triple_contexts.txt
├── valid
|   ├── siamese_contexts.txt
|   └── triple_contexts.txt
├── test
|   ├── knn-siamese_contexts.txt
|   ├── knn_triple_contexts.txt
|   ├── siamese_contexts.txt
|   └── triple_contexts.txt
└── skipgram-vec200-mincount5-win5.bin
└── fasttext-vec200-mincount5-win5.bin
└── in_vovab (build during training)
```
In each sub-folder,

* ```siamese_contexts.txt``` file contains entities and contexts for the siamese architecture. Each line has five columns, seperated by \t:
```entity_a \t entity_b \t context_a1@@context_a2...context_an \t context_b1@@context_b2@@...@@context_bn \t label```.

* ```entity_a``` and ```entity_b``` indicate two entities. e.g. ```u.s._government||m.01bqks||``` and ```united_states||m.01bqks||```.
* The next two columns indicate the contexts of two entities. e.g. ```context_a1@@context_a2...context_an``` indicates n pieces of contexts where ```entity_a``` is mentioned. ```@@``` is used to seperate contexts.
* ```label``` is a binary value indicating synonymity.

* ```triple_contexts.txt``` file contains entities and contexts for the triplet architecture. Each line has six columns, seperated by \t:
```entity_a \t entity_pos \t entity_neg \t context_a1@@context_a2...context_an \t context_pos_1@@context_pos_2@@...@@context_pos_p \t context_neg_1@@context_neg_2@@...@@context_neg_q```.

where ``entity_a`` denotes one entity and ```entity_pos``` denotes a synonym entity of ``entity_a`` and ```entity_neg``` as a negative sample of ``entity_a``.

* ```*-vec200-mincount5-win5.bin``` is a binary file stores the pre-trained word embedding trained using the corpus in the dataset.

* ```in_vocab``` is a vocabulary file generated automatically during training.

### Download
Pre-trained word vectors and datasets can be downloaded here:

| Dataset | Link |
| ------------- | ------------- |
| Wikipedia + Freebase | https://drive.google.com/open?id=1uX4KU6ws9xIIJjfpH2He-Yl5sPLYV0ws |
| PubMed + UMLS | https://drive.google.com/open?id=1cWHhXVd_Pb4N3EFdpvn4Clk6HVeWKVfF |

### Work on your own data
Prepare and organize your dataset in a folder according to the [format](#format) and put it under ```./input_data/``` and use `--dataset=foldername` during training.

For example, your dataset is `./input_data/mydata`, then you need to use the flag `--dataset=mydata` for ```train_triplet.py```.

Your dataset should be seperated to three folders - train, test, and valid, which is named 'train', 'test', and 'valid' by default setting of ```train_triplet.py``` or ```train_siamese.py```.

## Reference
```
@inproceedings{zhang2020entity,
title={Entity Synonym Discovery via Multipiece Bilateral Context Matching},
author={Zhang, Chenwei and Li, Yaliang and Du, Nan and Fan, Wei and Yu, Philip S},
booktitle={Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI)},
year={2020}
}
```