Paper: NTNU-CORE: Combining strong features for semantic similarity

ACL ID S13-1008
Title NTNU-CORE: Combining strong features for semantic similarity
Venue Joint Conference on Lexical and Computational Semantics
Year 2013

The paper outlines the work carried out at NTNU as part of the *SEM?13 shared task on Semantic Textual Similarity, using an ap- proach which combines shallow textual, dis- tributional and knowledge-based features by a support vector regression model. Feature sets include (1) aggregated similarity based on named entity recognition with WordNet and Levenshtein distance through the calcula- tion of maximum weighted bipartite graphs; (2) higher order word co-occurrence simi- larity using a novel method called ?Multi- sense Random Indexing?; (3) deeper seman- tic relations based on the RelEx semantic dependency relationship extraction system; (4) graph edit-distance on dependency trees; (5) reused features of the TakeLab and DKPro systems from the STS?12 shared task. The NTNU systems obtained 9...