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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15838
Title: Unilateral Weighted Jaccard Coefficient for NLP
Authors: Santisteban Pablo, Julio Omar
Tejada Cárcamo, Javier
Keywords: Entropy;Pattern recognition;Semantics;distance;Jaccard;Jaccard coefficientsMeasure of uncertainty;Pattern recognition problems;Semantic relationships;similarity;uncertainty;Artificial
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987810721&doi=10.1109%2fMICAI.2015.9&partnerID=40&md5=9a58bc99be1464180a13914a7ecab715
Abstract: Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various similarity measures are categorized in both syntactic and semantic relationships. In this paper we present a novel similarity, Unilateral Weighted Jaccard Coefficient (uwJaccard), which takes into consideration not only the space among two points but also the semantics among them in a distributional semantic model, the Unilateral Weighted Jaccard Coefficient provides a measure of uncertainty which will be able to measure the uncertainty among sentences such as "man bites dog" and "dog bites man". © 2015 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15838
ISBN: urn:isbn:9781509003235
Appears in Collections:Artículos - Ciencia de la computación

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