使用word embedding处理multi-label 文本分类任务

1. 首页
基于主题的Android APP商店
评论提取工具设计与实现

2. 目录
目 录
• 数据集
• 词表示
• 多分类

3. 数据集
数据集
• User Experience
• "I use this app almost every weekend while exploring back
roads and trails by motorcycle. Functionality is excellent
while on the road as well as using the data to review later
using Google Earth"
• Bug report
• "After the new update, my mobile freezes after I've been
using the app for a few minutes"
• Feature/Improvement request
• "I wish you could add a link that would allow me to share
the information with my Facebook friends”
• Rating
• “I cannot believe how amazing the new changes are.”

4. sql

5. 传统分类
Text Classification
传统方法(Benchmark):
• Bag-of-words models
• TF-IDF weighting
不足：
• 不考虑 word order
• Semantic ？

6. 词表示
Word Representation
词表示
词向量：
• word2vec
• GloVe
优势：
• word order
• Semantic ！

7. Train Word Vector
• Word2vec
• Markolov

10. Python Gensim
in 0.015471 0.021997 0.010186 -0.005084 0.017070
0.013390 -0.021693 -0.023888 0.001165 0.020663
0.024331 -0.003050 0.003170 -0.017819 -0.008096 -
0.015538 0.020433 0.006722 0.002464 0.016418

12. Multi-label
Multi-label
text classification
• Binary Relevance ?
• Feedforward Networks ？

13. Convolutional Neural Network (CNN)：
• Convolutional Neural Networks for Sentence
Classification, Kim (2014)
• Pre-trained word vector

14. cnn

15. Train Wordvector
Deep Learning
• Theano/Tensorflow
• Keras

16. THANKS

17. Reference
[1] Analyzing and automatically labelling the types of userissues that are
raised in mobile app reviews,Stuart McIlroy , Nasir Ali, Hammad Khalid,
Ahmed E. Hassan,Springer Science+Business Media New York 2015
[2] Bug Report, Feature Request, or Simply Praise?On Automatically
Classifying App Reviews,Walid Maalej,Hadeer Nabil,Arxiv 2015