Hierarchical Multi-Label Text Classification

This repository is my research project, and it is accepted by CIKM'19. The paper is already published.

The main objective of the project is to solve the hierarchical multi-label text classification (HMTC) problem. Different from the multi-label text classification, HMTC assigns each instance (object) into multiple categories and these categories are stored in a hierarchy structure, is a fundamental but challenging task of numerous applications.

Requirements

Python 3.6
Tensorflow 1.15.0
Tensorboard 1.15.0
Sklearn 0.19.1
Numpy 1.16.2
Gensim 3.8.3
Tqdm 4.49.0

Introduction

Many real-world applications organize data in a hierarchical structure, where classes are specialized into subclasses or grouped into superclasses. For example, an electronic document (e.g. web-pages, digital libraries, patents and e-mails) is associated with multiple categories and all these categories are stored hierarchically in a tree or Direct Acyclic Graph (DAG).

It provides an elegant way to show the characteristics of data and a multi-dimensional perspective to tackle the classification problem via hierarchy structure.

The Figure shows an example of predefined labels in hierarchical multi-label classification of documents in patent texts.

Documents are shown as colored rectangles, labels as rounded rectangles.
Circles in the rounded rectangles indicate that the corresponding document has been assigned the label.
Arrows indicate a hierarchical structure between labels.

Project

The project structure is below:

.
├── HARNN
│   ├── train.py
│   ├── layers.py
│   ├── ham.py
│   ├── test.py
│   └── visualization.py
├── utils
│   ├── checkmate.py
│   ├── param_parser.py
│   └── data_helpers.py
├── data
│   ├── word2vec_100.model.* [Need Download]
│   ├── Test_sample.json
│   ├── Train_sample.json
│   └── Validation_sample.json
├── LICENSE
├── README.md
└── requirements.txt

Data

You can download the Patent Dataset used in the paper. And the Word2vec model file (dim=100) is also uploaded. Make sure they are under the /data folder.

(As for Education Dataset, they may attract copyright protection under China law. Thus, there is no details of dataset.)

Text Segment

You can use nltk package if you are going to deal with the English text data.
You can use jieba package if you are going to deal with the Chinese text data.

Data Format

See data format in data folder which including the data sample files. For example:

{"id": "3930316", "title": ["sighting", "firearm"], "abstract": ["rear", "sight", "firearm", "ha", "peephole", "device", "formed", "hollow", "tube", "end", "closed", "peephole"], "section": [5], "subsection": [104], "group": [512], "subgroup": [6535], "labels": [5, 113, 649, 7333]}

"id": just the id.
"title" & "abstract": it's the word segment (after cleaning stopwords).
"section": it's the first level category index.
"subsection": it's the second level category index.
"group": it's the third level category index.
"subgroup": it's the fourth level category index.
"labels": it's the total category which add the index offset. (I will explain that later)

How to construct the data?

Use the figure in Introduction as example, now I will explain how to construct the label index.

Step 1: Figure has 3 categories, you should index this 3 categories first, like:

{"Chemistry": 0, "Physics": 1, "Electricity": 2, "XXX": 3, ..., "XXX": N-1}

Note: N is the total number of your categories.

Step 2: You index the next level, like:

{"Inorganic Chemistry": 0, "Organic Chemistry": 1, "Nuclear Physics": 2, "Material analysis": 3, "XXX": 4, ..., "XXX": M-1}

Note: M is the total number of your subcategories.

Step 3: You index the third level, like:

{"Steroids": 0, "Peptides": 1, "Heterocyclic Compounds": 2, ..., "XXX": K-1}

Note: K is the total number of your level-3 categories.

Step 4: If you have the fourth level or deeper level, index them.

Step 5: Now Suppose you have one record:

{"id": "1", "title": ["tokens"], "abstract": ["tokens"], "section": [0, 1], "subsection": [0，1, 2, 3], "group": [0，1, 2, 3], "labels": [0, 1, 0+N, 1+N, 2+N, 3+N, 0+N+M, 1+N+M, 2+N+M, 3+N+M]}

Assume that your total category number of level-1 is 10 (N=10), of level-2 is 100 (M=100). N & M is the offset for the labels attribute.

the record should be construed as:

{"id": "1", "hashtags": ["token"], "section": [0, 1], "subsection": [0, 1, 2, 3], "group": [0, 1, 2, 3], "labels": [0, 1, 10, 11, 12, 13, 110, 111, 112, 113]}

This repository can be used in other datasets (text classification) in two ways:

Modify your datasets into the same format of the sample.
Modify the data preprocess code in data_helpers.py.

Anyway, it should depend on what your data and task are.

Pre-trained Word Vectors

You can pre-training your word vectors(based on your corpus) in many ways:

Use gensim package to pre-train data.
Use glove tools to pre-train data.
Even can use a fasttext network to pre-train data.

Usage

See Usage.

Network Structure

Reference

If you want to follow the paper or utilize the code, please note the following info in your work:

@inproceedings{huang2019hierarchical,
  author    = {Wei Huang and
               Enhong Chen and
               Qi Liu and
               Yuying Chen and
               Zai Huang and
               Yang Liu and
               Zhou Zhao and
               Dan Zhang and
               Shijin Wang},
  title     = {Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach},
  booktitle = {Proceedings of the 28th {ACM} {CIKM} International Conference on Information and Knowledge Management, {CIKM} 2019, Beijing, CHINA, Nov 3-7, 2019},
  pages     = {1051--1060},
  year      = {2019},
}