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cnn-question-classification-keras

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Chinese Question Classifier (Keras Implementation) on BQuLD

cnn-question-classification-keras 최신버젼 다운로드

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# Recurrent Convolutional Neural Networks for Chinese Question Classification on BQuLD

A deep learning-based Chinese question classifier (Keras implementation) on BQuLD

## Contents
* [Model Architecture Overview](#model-architecture-overview)
* [Bilingual Question Labelling Dataset (BQuLD)](#bilingual-question-labelling-dataset-bquld)
* [Embedding Preparation](#embedding-preparation)
* [Result](#result)

## Model Architecture Overview
![Alt text](https://raw.githubusercontent.com/tim5go/cnn-question-classification-keras/master/img/rcnn_p1.png)
 
For more details  [Click Here](http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9745).

## Bilingual Question Labelling Dataset (BQuLD)
This dataset is a bilingual (traditional Chinese & English) question labelling dataset designed for NLP researchers. 
The questinon type definition is borrowed from [Intelligent Agent Systems Lab](http://iasl.iis.sinica.edu.tw/):
![Alt text](https://raw.githubusercontent.com/tim5go/cnn-question-classification-keras/master/img/question_type_p1.png)

The dataset originally consists of 1216 pairs of question and question label, which first published by the author of this GitHub [tim5go](https://github.com/tim5go) 
There are 9 question types in total, namely:

0.  NUMBER
1.  PERSON
2.  LOCATION
3.  ORGANIZATION
4.  ARTIFACT
5.  TIME
6.  PROCEDURE
7.  AFFIRMATION
8.  CAUSALITY

## Embedding Preparation
In my experiment, I built a word2vec model on 全網新聞數據(SogouCA) [Sogou Labs](http://www.sogou.com/labs/resource/ca.php)

For example, in Linux:

1. clean XML tag
```
$ cat news_tensite_xml.dat | iconv -f gbk -t utf-8 -c | grep "<content>" 
 | sed 's\<content>\\' | sed 's\</content>\\' > corpus.txt
```

2. word segmentation using [LTP command line](https://github.com/HIT-SCIR/ltp)
```
$ cws_cmdline --threads 4 --input corpus.txt --segmentor-model cws.model > corpus.seg.txt
```
3. simplified to traditional Chinese conversion using [OpenCC](https://github.com/BYVoid/OpenCC)
```
$ opencc -i corpus.seg.txt -o corpus_trad.txt -c s2t.json
```

4. word2Vec training using [Google Word2vec](https://code.google.com/archive/p/word2vec/source/default/source)
```
$ nohup ./word2vec -train corpus_trad.txt -output sogou_vectors.bin -cbow 0 
  -size 200 -window 10 -negative 5 -hs 0 -sample 1e-4 -threads 24 -binary 1 -iter 20 -min-count 1 &
```

## Result

Training Loss | Training Accuracy | Validation Loss| Validation Accuracy 
--- | --- | --- | --- 
0.7000 | 87.11% | 0.8945 | 77.87%

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