SIGN-IN

Publication: Methods for Augmenting Semantic Models with Structural Information for Text Classification

All || By Area || By Year

Title Methods for Augmenting Semantic Models with Structural Information for Text Classification
Authors/Editors* J Fishbein, C Eliasmith
Where published* ECIR
How published* Proceedings
Year* 2008
Volume
Number
Pages
Publisher
Keywords
Link
Abstract
Current representation schemes for automatic text classifi- cation treat documents as syntactically unstructured collections of words or `concepts'. Past attempts to encode syntactic structure have treated part-of-speech information as another word-like feature, but have been shown to be less effective than non-structural approaches. Here, we investigate three methods to augment semantic modelling with syntactic structure, which encode the structure across all features of the document vector while preserving text semantics. We present classification results for these methods versus the Bag-of-Concepts semantic modelling representation to determine which method best improves classification scores.
Go to Computational Neuroscience
Back to page 54 of list