Recurrent Neural Networks for Robust Real-World Text Classification
Garen Arevian · 2007
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.