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

Introduction

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

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.

Prediction

python predict.py conf/train.json data/predict.json

predict.json should be of json format, while each instance has a dummy label like "其他" or any other label in label map.
eval.model_dir is the model to predict.
eval.top_k is the number of labels to output.
eval.threshold is the probability threshold.

The predict info will be outputed in predict.txt.

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

Dataset	Taxonomy	#Label	#Training	#Test
RCV1	Tree	103	23,149	781,265
Yelp	DAG	539	87,375	37,265

RCV1: Lewis et al., 2004
Yelp: Yelp

1. Compare with state-of-the-art

Text Encoders	Micro-F1 on RCV1	Micro-F1 on Yelp
HR-DGCNN (Peng et al., 2018)	0.7610	-
HMCN (Wehrmann et al., 2018)	0.8080	0.6640
Ours	0.8313	0.6704

HR-DGCNN: Peng et al., 2018
HMCN: Wehrmann et al., 2018

2. Different text encoders

Text Encoders	RCV1		Yelp
	Micro-F1	Macro-F1	Micro-F1	Macro-F1
TextCNN	0.7717	0.5246	0.6281	0.3657
TextRNN	0.8152	0.5458	0.6704	0.4059
RCNN	0.8313	0.6047	0.6569	0.3951
FastText	0.6887	0.2701	0.6031	0.2323
DRNN	0.7846	0.5147	0.6579	0.4401
DPCNN	0.8220	0.5609	0.5671	0.2393
VDCNN	0.7263	0.3860	0.6395	0.4035
AttentiveConvNet	0.7533	0.4373	0.6367	0.4040
RegionEmbedding	0.7780	0.4888	0.6601	0.4514
Transformer	0.7603	0.4274	0.6533	0.4121
Star-Transformer	0.7668	0.4840	0.6482	0.3895

performance got with 300d pretrained glove embedding

3. Hierarchical vs Flat

Text Encoders	Hierarchical		Flat
	Micro-F1	Macro-F1	Micro-F1	Macro-F1
TextCNN	0.7717	0.5246	0.7367	0.4224
TextRNN	0.8152	0.5458	0.7546	0.4505
RCNN	0.8313	0.6047	0.7955	0.5123
FastText	0.6887	0.2701	0.6865	0.2816
DRNN	0.7846	0.5147	0.7506	0.4450
DPCNN	0.8220	0.5609	0.7423	0.4261
VDCNN	0.7263	0.3860	0.7110	0.3593
AttentiveConvNet	0.7533	0.4373	0.7511	0.4286
RegionEmbedding	0.7780	0.4888	0.7640	0.4617
Transformer	0.7603	0.4274	0.7602	0.4339
Star-Transformer	0.7668	0.4840	0.7618	0.4745

Acknowledgement

Some public codes are referenced by our toolkit:

Update

2019-04-29, init version

sq6ra/NeuralNLP-NeuralClassifier