HiAGM: Hierarchy-Aware Global Model for Hierarchical Text Classification

This repository implements the hierarchy-aware structure encoders for mutual interaction between label space and text features. This work has been accepted as the long paper 'Hierarchy-Aware Global Model for Hierarchical Text Classification' in ACL 2020. The dataset splits of NYTimes (New York Times) and WoS (Web of Science) are proposed in this repository.

Hierarchy-Aware Global Model

The hierarchy-aware global model improves the conventional text classification model with prior knowledge of the predefined hierarchical structure. The project folder consists of following parts:

config: config files (json format)
data: data dir, could be changed in config file (with sample data)
data_modules: Dataset / DataLoader / Collator / Vocab
helper: Configure / Hierarchy_Statistic / Logger / Utils
models: StructureModel / EmbeddingLayer / TextEncoder / TextPropagation (HiAGM-TP) / Multi-Label Attention (HiAGM-LA)
train_modules: Criterions / EvaluationMetrics / Trainer

Hierarchy-Aware Structure Encoder

Bidirectional TreeLSTM: weighted_tree_lstm.py & tree.py
Hierarchy-GCN: graphcnn.py

Setup

Python >= 3.6
torch >= 0.4.1
numpy >= 1.17.4

Preprocess

data_modules.preprocess

transform to json format file {'token': List[str], 'label': List[str]}
clean stopwords
RCV1-V2: The preprocess code could refer to the repository of reuters_loader.
NYTimes & WoS: data.preprocess_nyt & data.preprocess_wos. Please download the origin datasets and then use these codes to preprocess for HTC.

Prior Probability

helper.hierarchical_statistic
Note that first change the Root.child List
calculate the prior probability between parent-child pair in train dataset

Train

python train.py config/gcn-rcv1-v2.json

optimizer -> train.set_optimizer: default torch.optim.Adam
learning rate decay schedule callback -> train_modules.trainer.update_lr
earlystop callback -> train.py
Hyper-parameters are set in config.train

Citation

Please cite our ACL 2020 paper:

@article{jie2020hierarchy,  
 title={Hierarchy-Aware Global Model for Hierarchical Text Classification},  
 author={Jie Zhou, Chunping Ma, Dingkun Long, Guangwei Xu, Ning Ding, Haoyu Zhang, Pengjun Xie, Gongshen Liu},  
 booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)},
 year={2020}  
}

liuyeah/HiAGM