Regression based approaches for detecting and measuring textual similarity

Sandip Sarkar, Partha Pakray, Dipankar Das, Alexander Gelbukh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

© 2017, Springer International Publishing AG. Finding Semantic similarity is an important component in various fields such as information retrieval, question-answering system, machine translation and text summarization. This paper describes two different approaches to find semantic similarity on SemEval 2016 dataset. First method is based on lexical analysis whereas second method is based on distributed semantic approach. Both approaches are trained using feed-forward neural network and layer-recurrent network to predict the similarity score.
Original languageAmerican English
Title of host publicationRegression based approaches for detecting and measuring textual similarity
Pages144-152
Number of pages128
ISBN (Electronic)9783319581293
DOIs
StatePublished - 1 Jan 2017
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2018 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10089 LNAI
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/18 → …

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Sarkar, S., Pakray, P., Das, D., & Gelbukh, A. (2017). Regression based approaches for detecting and measuring textual similarity. In Regression based approaches for detecting and measuring textual similarity (pp. 144-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10089 LNAI). https://doi.org/10.1007/978-3-319-58130-9_14