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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)
HMCN(Wehrmann et al.,2018)

Requirement

Python 3
PyTorch 0.4+
Numpy 1.14.3+

System Architecture

NeuralClassifier Architecture

Usage

Training

How to train a non-hierarchical classifier

python train.py conf/train.json

set task_info.hierarchical = false.
model_name can be FastText、TextCNN、TextRNN、TextRCNN、DRNN、VDCNN、DPCNN、AttentiveConvNet、Transformer.

How to train a hierarchical classifier using hierarchial penalty

python train.py conf/train.hierar.json

set task_info.hierarchical = true.
model_name can be FastText、TextCNN、TextRNN、TextRCNN、DRNN、VDCNN、DPCNN、AttentiveConvNet、Transformer

How to train a hierarchical classifier with HMCN

python train.py conf/train.hmcn.json

set task_info.hierarchical = false.
set model_name = HMCN

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

<table> <tr><th>Dataset<th>Taxonomy<th>#Label<th>#Training<th>#Test <tr><td>RCV1<td>Tree<td>103<td>23,149<td>781,265 <tr><td>Yelp<td>DAG<td>539<td>87,375<td>37,265 </table>

RCV1: Lewis et al., 2004
Yelp: Yelp

1. Compare with state-of-the-art

<table> <tr><th>Text Encoders<th>Micro-F1 on RCV1<th>Micro-F1 on Yelp <tr><td>HR-DGCNN (Peng et al., 2018)<td>0.7610<td>- <tr><td>HMCN (Wehrmann et al., 2018)<td>0.8080<td>0.6640 <tr><td>Ours<td><strong>0.8313</strong><td><strong>0.6704</strong> </table>

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

2. Different text encoders

<table> <tr><th row_span='2'>Text Encoders<th colspan='2'>RCV1<th colspan='2'>Yelp <tr><td><th>Micro-F1<th>Macro-F1<th>Micro-F1<th>Macro-F1 <tr><td>TextCNN<td>0.7717<td>0.5246<td>0.6281<td>0.3657 <tr><td>TextRNN<td>0.8152<td>0.5458<td><strong>0.6704</strong><td>0.4059 <tr><td>RCNN<td><strong>0.8313</strong><td><strong>0.6047</strong><td>0.6569<td>0.3951 <tr><td>FastText<td>0.6887<td>0.2701 <td>0.6031<td>0.2323 <tr><td>DRNN<td>0.7846 <td>0.5147<td>0.6579<td>0.4401 <tr><td>DPCNN<td>0.8220 <td>0.5609 <td>0.5671 <td>0.2393 <tr><td>VDCNN<td>0.7263 <td>0.3860<td>0.6395<td>0.4035 <tr><td>AttentiveConvNet<td>0.7533<td>0.4373<td>0.6367<td>0.4040 <tr><td>RegionEmbedding<td>0.7780 <td>0.4888 <td>0.6601<td><strong>0.4514</strong> <tr><td>Transformer<td>0.7603 <td>0.4274<td>0.6533<td>0.4121 <tr><td>Star-Transformer<td>0.7668 <td>0.4840<td>0.6482<td>0.3895 </table>

performance got with 300d pretrained glove embedding

3. Hierarchical vs Flat

<table> <tr><th row_span='2'>Text Encoders<th colspan='2'>Hierarchical<th colspan='2'>Flat <tr><td><th>Micro-F1<th>Macro-F1<th>Micro-F1<th>Macro-F1 <tr><td>TextCNN<td>0.7717<td>0.5246<td>0.7367<td>0.4224 <tr><td>TextRNN<td>0.8152<td>0.5458<td>0.7546 <td>0.4505 <tr><td>RCNN<td><strong>0.8313</strong><td><strong>0.6047</strong><td><strong>0.7955</strong><td><strong>0.5123</strong> <tr><td>FastText<td>0.6887<td>0.2701 <td>0.6865<td>0.2816 <tr><td>DRNN<td>0.7846 <td>0.5147<td>0.7506<td>0.4450 <tr><td>DPCNN<td>0.8220 <td>0.5609 <td>0.7423 <td>0.4261 <tr><td>VDCNN<td>0.7263 <td>0.3860<td>0.7110<td>0.3593 <tr><td>AttentiveConvNet<td>0.7533<td>0.4373<td>0.7511<td>0.4286 <tr><td>RegionEmbedding<td>0.7780 <td>0.4888 <td>0.7640<td>0.4617 <tr><td>Transformer<td>0.7603 <td>0.4274<td>0.7602<td>0.4339 <tr><td>Star-Transformer<td>0.7668 <td>0.4840<td>0.7618<td>0.4745 </table>

Acknowledgement

Some public codes are referenced by our toolkit:

Update

2019-04-29, init version