All-Topology, Semi-Abstract Syntactic Features for Text Categorization

Good performance on Text Classification (TC) tasks depends on effective and statistically significant features. Typically, the simple bag-of-words representation is widely used because unigram counts are more likely to be significant compared to more compound features. This research explores the idea that the major cause of poor performance of some complex features is sparsity. Syntactic features are usually complex being made up of both lexical and syntactic information. This paper introduces the use of a class of automatically extractable, syntactic features to the TC task. These features are based on subtrees of parse trees. As such, a large number of these features are generated. Our results suggest that generating a diverse set of these features may help in increasing performance. Partial abstraction of the features also seems to boost performance by counteracting sparsity. We will show that various subsets of our syntactic features do outperform the bag-of-words representation alone.