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RESEARCH PRODUCT
Semantic Word Error Rate for Sentence Similarity
Giorgio VassalloAgnese AugelloCarmelo SpicciaGiovanni Pilatosubject
Machine translationComputer scienceSpeech recognitionWord error rate02 engineering and technologycomputer.software_genreParaphrase030507 speech-language pathology & audiology03 medical and health sciencesSemantic similarityArtificial IntelligenceLSAWord Error Rate0202 electrical engineering electronic engineering information engineeringsentence resemblanceEquivalence (formal languages)Latent Semantic AnalysiSemantic Word Error Ratesentence similarity measureSWERbusiness.industryLatent semantic analysisSentence SimilaritySemantic ComputingCognitionAutomatic summarizationComputer Networks and Communicationword relatedne020201 artificial intelligence & image processingArtificial intelligence0305 other medical sciencebusinesscomputerNatural language processingWERInformation Systemsdescription
Sentence similarity measures have applications in several tasks, including: Machine Translation, Paraphrase Iden- tification, Speech Recognition, Question-answering and Text Summarization. However, measures designed for these tasks are aimed at assessing equivalence rather than resemblance, partly departing from human cognition of similarity. While this is reasonable for these activities, it hinders the applicability of sentence similarity measures to other tasks. We therefore propose a new sentence similarity measure specifically designed for resemblance evaluation, in order to cover these fields better. Experimental results are discussed.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-02-01 | 2016 IEEE Tenth International Conference on Semantic Computing (ICSC) |