ATA-Sem: Chunk-based Determination of Semantic Text Similarity
Demetrios G. Glinos · 2012
This paper describes investigations into using syntactic chunk information as the basis for determining the similarity of candidate texts at the semantic level. Two approaches were considered. The first was a corpus-based method that extracted lexical and semantic features from pairs of chunks from each sentence that were associated through a chunk alignment algorithm. The features were used as input to a classifier trained on the same features extracted from a corpus of gold standard training data. The second approach involved breadthfirst chunk association and the application of a rule-based scoring algorithm. Both approaches were evaluated against the test data for the SemEval 2012 Semantic Text Similarity task. The results show that the rule-based chunk approach is superior. 1