An implement of EMNLP 2019 paper "Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification" and its extension "HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification" (TOIS 2021).
Thank you for your interest in our work! 😄
- Anaconda3 (python 3.6)
- Pytorch 1.3.1
- gensim 3.6.0
cd ./model/code/
python train.py
You may change the dataset by modifying the variable "dataset = 'example'" in the top of the code "train.py" or use arguments (see train.py).
Our datasets can be downloaded from Google Drive. PS: I have accidentally deleted some files, but I tried to restore them, hope they will run correctly.
The following files are required:
./model/data/YourData/
---- YourData.cites // the adjcencies
---- YourData.content.text // the features of texts
---- YourData.content.entity // the features of entities
---- YourData.content.topic // the features of topics
---- train.map // the index of the training node
---- vali.map // the index of the validation nodes
---- test.map // the index of the testing nodes
The format is as following:
-
YourData.cites
Each line contains an edge: "idx1\tidx2\n". eg: "98 13"
-
YourData.content.text
Each line contains a node: "idx\t[features]\t[category]\n", note that the [features] is a list of floats with '\t' as the delimiter. eg: "59 1.0 0.5 0.751 0.0 0.659 0.0 computers" If used for multi-label classification, [category] must be one-hot with space as delimiter, eg: "59 1.0 0.5 0.751 0.0 0.659 0.0 0 1 1 0 1 0".
-
YourData.content.entity
Similar with .text, just change the [category] to "entity". eg: "13 0.0 0.0 1.0 0.0 0.0 entity"
-
YourData.content.topic
Similar with .text, just change the [category] to "topic". eg: "64 0.10 1.21 8.09 0.10 topic"
-
*.map
Each line contains an index: "idx\n". eg: "98"
You can see the example in ./model/data/example/*
A simple data preprocessing code is provided. Successfully running it requires a token of tagme's account (my personal token is provided in tagme.py, but may be invalid in the future), Wikipedia's entity descriptions, and a word2vec model containing entity embeddings. You can prepare them yourself or obtain our files from Google Drive and unzip them to ./data/ .
Then, you should prepare a data file like ./data/example/example.txt, whose format is: "[idx]\t[category]\t[content]\n".
Finally, modify the variable "dataset = 'example'" in the top of following codes and run:
python tagMe.py
python build_network.py
python build_features.py
python build_data.py
If you just wanna use the HGAT model as a graph neural network, you can just prepare some files following the above format:
./model/data/YourData/
---- YourData.cites // the adjcencies
---- YourData.content.* // the features of *, namely node_type1, node_type2, ...
---- train.map // the index of the training node
---- vali.map // the index of the validation nodes
---- test.map // the index of the testing nodes
And change the "load_data()" in ./model/code/utils.py
type_list = [node_type1, node_type2, ...]
type_have_label = node_type
See the codes for more details.
If you make advantage of the HGAT model in your research, please cite the following in your manuscript:
@article{yang2021hgat,
author = {Yang, Tianchi and Hu, Linmei and Shi, Chuan and Ji, Houye and Li, Xiaoli and Nie, Liqiang},
title = {HGAT: Heterogeneous Graph Attention Networks for Semi-Supervised Short Text Classification},
year = {2021},
publisher = {Association for Computing Machinery},
volume = {39},
number = {3},
doi = {10.1145/3450352},
journal = {ACM Transactions on Information Systems},
month = may,
articleno = {32},
numpages = {29},
}
@inproceedings{linmei2019heterogeneous,
title={Heterogeneous graph attention networks for semi-supervised short text classification},
author={Linmei, Hu and Yang, Tianchi and Shi, Chuan and Ji, Houye and Li, Xiaoli},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={4823--4832},
year={2019}
}
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