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
Session
Year 2013
Authors

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...