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ACL 2019: Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
| Date | Stars |
|---|---|
| 2026-07-24 | 291 |
| 2026-07-25 | 291 |
| 2026-07-28 | 291 |
| 2026-07-30 | 291 |
| 2026-08-06 | 291 |
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<h1 align="center">
WordGCN
</h1>
<h4 align="center">Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks </h4>
<p align="center">
<a href="https://www.aclweb.org/anthology/P19-1320/"><img src="http://img.shields.io/badge/ACL-2019-4b44ce.svg"></a>
<a href="https://arxiv.org/abs/1809.04283"><img src="http://img.shields.io/badge/Paper-PDF-red.svg"></a>
<a href="https://shikhar-vashishth.github.io/assets/pdf/wordgcn_supp.pdf"><img src="http://img.shields.io/badge/Supplementary-PDF-B31B1B.svg"></a>
<a href="https://shikhar-vashishth.github.io/assets/pdf/wordgcn_poster.pdf"><img src="http://img.shields.io/badge/Poster-PDF-orange.svg"></a>
<a href="https://github.com/malllabiisc/WordGCN/blob/master/LICENSE">
<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg">
</a>
</p>
<h2 align="center">
Overview of WordGCN
</h2>
<h4 align="center">
<img align="center" src="./images/syngcn_model.png" alt="...">
</h4>
*Overview of SynGCN: SynGCN employs Graph Convolution Network for utilizing dependency context for learning word embeddings. For each word in vocabulary, the model learns its representation by aiming to predict each word based on its dependency context encoded using GCNs. Please refer Section 5 of the paper for more details.*
### Dependencies
- Compatible with TensorFlow 1.x and Python 3.x.
- Dependencies can be installed using `requirements.txt`.
- `pip3 install -r requirements.txt`
- Install [word-embedding-benchmarks](https://github.com/kudkudak/word-embeddings-benchmarks) used for evaluating learned embeddings.
- The test and valid dataset splits used in the paper can be downloaded from [this link](https://drive.google.com/open?id=1VMyddIOgmkskAFN2BvI6c49Y63SHjNfF). Replace the original `~/web_data` folder with the provided one.
- For switching between valid and test split execute `python switch_evaluation_data.py -split <valid/valid>`
### Dataset:
* We used [Wikipedia corpus](https://dumps.wikimedia.org/enwiki/20180301/). The processed version can be downloaded from [here](https://drive.google.com/file/d/1iFpuKFpDnXCD9QpUw8wStG3ndKl7-KwX/view?usp=sharing) or using the script below:
```shell
pip install gdown
gdown --id 1iFpuKFpDnXCD9QpUw8wStG3ndKl7-KwX -O data.zip
unzip data.zip
rm data.zip
```
* The processed dataset includes:
* `voc2id.txt` mapping of words to to their unique identifiers.
* `id2freq.txt` contains frequency of words in the corpus.
* `de2id.txt` mapping of dependency relations to their unique identifiers.
* `data.txt` contains the entire Wikipedia corpus with each sentence of corpus stored in the following format:
```java
<num_words> <num_dep_rels> tok1 tok2 tok3 ... tokn dep_e1 dep_e2 .... dep_em
```
- Here, `num_words` is the number of words and `num_dep_rels` denotes the number of dependency relations in the sentence.
- `tok_1, tok_2 ...` is the list of tokens in the sentence and `dep_e1, dep_e2 ...`is the list of dependency relations where each is of form `source_token|destination_token|dep_rel_label`.
### Training SynGCN embeddings:
- Download the processed Wikipedia corpus ([link](https://drive.google.com/file/d/1iFpuKFpDnXCD9QpUw8wStG3ndKl7-KwX/view?usp=sharing)) and extract it in `./data` directory.
- Execute `make` to compile the C++ code for creating batches.
- To start training run:
```shell
python syngcn.py -name test_embeddings -gpu 0 -dump
-maxsentlen <max_sentence_length in your data.txt>
-maxdeplen <max_dependency_length in your data.txt>
-embed_dim 300
```
* The trained embeddings will be stored in `./embeddings` directory with the provided name `test_embeddings` .
* **Note:** As reported in TensorFlow issue [#13048](https://github.com/tensorflow/tensorflow/issues/13048). The current SynGCN's TF-based implementation is slow compared to [Mikolov's word2vec](https://github.com/tmExcerpt of 6,427 characters
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