Robust semantic text similarity using LSA, machine learning, and linguistic resources.
Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM 2013 and SemEval-2014 tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity...
| Published in: | Language Resources & Evaluation Vol. 50; no. 1; pp. 125 - 162 |
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| Main Authors: | , , , , , , |
| Format: | Article |
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Springer Nature
Mar2016
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=113139629&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 113139629 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2016 vid: 50 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 113139629 10.1007/s10579-015-9319-2 ppf: 125 ppct: 37 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: Robust semantic text similarity using LSA, machine learning, and linguistic resources. aug: au: Kashyap, Abhay Han, Lushan Yus, Roberto Sleeman, Jennifer Satyapanich, Taneeya Gandhi, Sunil Finin, Tim affil: University of Maryland, Baltimore County USA University of Zaragoza, Zaragoza Spain su: Latent semantic analysis Semantics Computational linguistics Multilingualism Machine learning Computer network resources sug: subj: Latent semantic analysis Semantics Computational linguistics Multilingualism Machine learning Computer network resources keyword: Semantic similarity Term alignment WordNet ab: Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM 2013 and SemEval-2014 tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines latent semantic analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of text. Additional wrappers and resources were used to handle task specific challenges that include processing Spanish text, comparing text sequences of different lengths, handling informal words and phrases, and matching words with sense definitions. In the *SEM 2013 task on Semantic Textual Similarity, our best performing system ranked first among the 89 submitted runs. In the SemEval-2014 task on Multilingual Semantic Textual Similarity, we ranked a close second in both the English and Spanish subtasks. In the SemEval-2014 task on Cross-Level Semantic Similarity, we ranked first in Sentence-Phrase, Phrase-Word, and Word-Sense subtasks and second in the Paragraph-Sentence subtask. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2016. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
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