Combining Context-relevant Features with Multi-stage Attention Network for Short Text Classification
- numpy==1.21.3
- requests==2.26.0
- scikit_learn==1.0.1
- spacy==3.2.3
- tagme==0.1.3
- torch==1.8.0+cu111
- torchtext==0.9.0
- tqdm==4.62.3
demo示例为TREC数据集，
# origin text \t concepts \t label
dist far denver aspen city market place area community num
city county modesto california city county city community town area jurisdiction state area jurisdiction place large state loc
desc galileo desc galileo hum
...
相关文件下载地址
运行 preprocess.py文件即可得到符合输入标准的数据集
Train & Dev & Test: Original dataset is randomly split into 80% for training and 20% for test. 20% of randomly selected training instances are used to form development set.
$ python main.py --epoch 100 --lr 2e-4 --train_data_path dataset/tagmynews.tsv --txt_embedding_path dataset/glove.6B.300d.txt --cpt_embedding_path dataset/glove.6B.300d.txt --embedding_dim 300 --train_batch_size 128 --hidden_size 64
More detailed configurations can be found in config.py, which is in utils folder.
Chen J, Hu Y, Liu J, et al. Deep Short Text Classification with Knowledge Powered Attention[J]. 2019.
The code is for research purpose only and released under the Apache License, Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0).
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent