DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation

Nikolaos Malandrakis, Elias Iosif, Vassiliki Prokopi, Alexandros Potamianos, Shrikanth Narayanan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

This paper describes our submission for the ∗SEM shared task of Semantic Textual Similarity. We estimate the semantic similarity between two sentences using regression models with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical semantic similarity between non-matching words, 3) string similarity metrics, 4) affective content similarity and 5) sentence length. Domain adaptation is applied in the form of independent models and a model selection strategy achieving a mean correlation of 0.47.

Original languageEnglish
Title of host publication*SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics
PublisherAssociation for Computational Linguistics (ACL)
Pages103-108
Number of pages6
ISBN (Electronic)9781937284480
Publication statusPublished - 2013
Externally publishedYes
Event2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013 - Atlanta, United States
Duration: 13 Jun 201314 Jun 2013

Publication series

Name*SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics
Volume1

Conference

Conference2nd Joint Conference on Lexical and Computational Semantics, *SEM 2013
Country/TerritoryUnited States
CityAtlanta
Period13/06/1314/06/13

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