Recurrent Neural Networks for Robust Real-World Text Classification

Garen Arevian
2007 IEEE/WIC/ACM International Conference on Web Intelligence

ABSTRACT

This paper explores the application of recurrent neural networks for
the task of robust text classification of a real-world benchmarking
corpus. There are many well-established approaches which are used for
text classification, but they fail to address the challenge from a more
multi-disciplinary viewpoint such as natural language processing and
artificial intelligence. The results demonstrate that these recurrent
neural networks can be a viable addition to the many techniques used in
web intelligence for tasks such as context sensitive email
classification and web site indexing.

Noteworthy

• Use of recurrent neural networks (Elman Networks) with a context layer, able to consider word orders
  
• Further references for NN's in text mining
• Title based semantic representation (at least pointers to prior literature on the topic)
• Word order was not important
  
• The claim made that NNs acn outperform other classifiers is very strong and does not hold in general