2026-04-17T13:53:10Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/278322025-12-22T13:32:16Zcom_2072_6col_2072_452952

urn:hdl:10230/27832 Towards definition extraction using conditional random fields Espinosa-Anke, Luis Paper presented at International Conference Recent Advances in Natural Language Processing RANLP 2013;2013 Sept 9-11; Hissar, Bulgaria. Definition Extraction (DE) and terminology are contributing to help structuring the overwhelming amount of information available. This article presents KESSI (Knowledge Extraction System for Scientific/nInterviews), a multilingual domainindependent machine-learning approach to the extraction of definitional knowledge, specifically oriented to scientific interviews. The DE task was approached as both a classification and a sequential labelling task. In the latter, figures of Precision, Recall and F-Measure were similar to human annotation, and suggest that combining structural, statistical and linguistic/nfeatures with Conditional Random Fields can contribute significantly to the development of DE systems. 2016-12-22T17:04:41Z 2016-12-22T17:04:41Z 2013 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Mitkov R, Angelova G, Boncheva K, editors. Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013;2013 Sept 9-11; Hissar, Bulgaria. Bulgaria: INCOMA, 2013. p.63-70 This document is under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License http://creativecommons.org/licenses/by-nc-sa/3.0/ info:eu-repo/semantics/openAccess INCOMA