Similarity estimation techniques from rounding algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
SemEval-2012 task 6: a pilot on semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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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 breadth-first 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.