/ Natural Language Processing

An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

NeuralClassifier

NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. It is built on PyTorch. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.

Support tasks

  • Binary-class text classifcation
  • Multi-class text classification
  • Multi-label text classification
  • Hiearchical (multi-label) text classification (HMC)

Support text encoders

TextCNN (Kim, 2014)
RCNN (Lai et al., 2015)
TextRNN (Liu et al., 2016)
FastText (Joulin et al., 2016)
VDCNN (Conneau et al., 2016)
DPCNN (Johnson and Zhang, 2017)
AttentiveConvNet (Yin and Schutze, 2017)
DRNN (Wang, 2018)
Region embedding (Qiao et al., 2018)
Transformer encoder (Vaswani et al., 2017)
Star-Transformer encoder (Guo et al., 2019)

Requirement

  • Python 3
  • PyTorch 0.4+
  • Numpy 1.14.3+

System Architecture

deeptext_arc

Usage

Training

python train.py conf/train.json

Detail configurations and explanations see Configuration.

The training info will be outputted in standard output and log.logger_file.

Evaluation

python eval.py conf/train.json
  • if eval.is_flat = false, hierarchical evaluation will be outputted.
  • eval.model_dir is the model to evaluate.
  • data.test_json_files is the input text file to evaluate.

The evaluation info will be outputed in eval.dir.

Input Data Format

JSON example:

{
    "doc_label": ["Computer--MachineLearning--DeepLearning", "Neuro--ComputationalNeuro"],
    "doc_token": ["I", "love", "deep", "learning"],
    "doc_keyword": ["deep learning"],
    "doc_topic": ["AI", "Machine learning"]
}

"doc_keyword" and "doc_topic" are optional.

Performance

0. Dataset

DatasetTaxonomy#Label#Training#Test
RCV1Tree10323,149781,265
YelpDAG53987,37537,265

1. Compare with state-of-the-art

Text EncodersMicro-F1 on RCV1Micro-F1 on Yelp
HR-DGCNN (Peng et al., 2018)0.7610-
HMCN (Wehrmann et al., 2018)0.80800.6640
Ours0.83130.6704

2. Different text encoders

Text EncodersRCV1Yelp
Micro-F1Macro-F1Micro-F1Macro-F1
TextCNN0.77170.52460.62810.3657
TextRNN0.81520.54580.67040.4059
RCNN0.83130.60470.65690.3951
FastText0.68870.2701 0.60310.2323
DRNN0.7846 0.51470.65790.4401
DPCNN0.8220 0.5609 0.5671 0.2393
VDCNN0.7263 0.38600.63950.4035
AttentiveConvNet0.75330.43730.63670.4040
RegionEmbedding0.7780 0.4888 0.66010.4514
Transformer0.7603 0.42740.65330.4121
Star-Transformer0.7668 0.48400.64820.3895

3. Hierarchical vs Flat

Text EncodersHierarchicalFlat
Micro-F1Macro-F1Micro-F1Macro-F1
TextCNN0.77170.52460.73670.4224
TextRNN0.81520.54580.7546 0.4505
RCNN0.83130.60470.79550.5123
FastText0.68870.2701 0.68650.2816
DRNN0.7846 0.51470.75060.4450
DPCNN0.8220 0.5609 0.7423 0.4261
VDCNN0.7263 0.38600.71100.3593
AttentiveConvNet0.75330.43730.75110.4286
RegionEmbedding0.7780 0.4888 0.76400.4617
Transformer0.7603 0.42740.76020.4339
Star-Transformer0.7668 0.48400.76180.4745

GitHub