Title Similarity-Based Feature Weighting for Text Categorization

In automated text categorization, a system analyzes a natural-language document to decide whether it belongs in one or more of a group of pre-defined categories. The typical approach is to represent the documents using feature vectors, and inductively generate a classifier based on a training set of documents and their manually-assigned categories. Such a process ignores information on word order, syntax, and other heuristics that might aid in identifying good features for categorization. Recently, more attention has been paid to using deeper natural language processing techniques to improve the performance of the standard classifiers. One such approach, which takes advantage of a previously-generated thesaurus of lexical similarities, is studied in this project. This system identifies key-words in the text by looking for terms with high similarity to the terms in the title field. A database of automatically-clustered dependency-based word similarities is used to identify the similar words. Experiments show increased weighting of key terms aids the effectiveness of text categorization for a number of topics in the standard Reuters newswire corpus.