Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/25279
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBorg, Claudia-
dc.contributor.authorRosner, Mike-
dc.contributor.authorPace, Gordon J.-
dc.date.accessioned2018-01-02T14:13:45Z-
dc.date.available2018-01-02T14:13:45Z-
dc.date.issued2009-
dc.identifier.citationBorg, C., Rosner, M., & Pace, G. (2009). Evolutionary algorithms for definition extraction. 1st Workshop on Definition Extraction, Borovets. 26-32.en_GB
dc.identifier.isbn9789544520137-
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/25279-
dc.description.abstractBooks and other text-based learning material contain implicit information which can aid the learner but which usually can only be accessed through a semantic analysis of the text. Definitions of new concepts appearing in the text are one such instance. If extracted and presented to the learner in form of a glossary, they can provide an excellent reference for the study of the main text. One way of extracting definitions is by reading through the text and annotating definitions manually — a tedious and boring job. In this paper, we explore the use of machine learning to extract definitions from non-technical texts, reducing human expert input to a minimum. We report on experiments we have conducted on the use of genetic programming to learn the typical linguistic forms of definitions and a genetic algorithm to learn the relative importance of these forms. Results are very positive, showing the feasibility of exploring further the use of these techniques in definition extraction. The genetic program is able to learn similar rules derived by a human linguistic expert, and the genetic algorithm is able to rank candidate definitions in an order of confidence.en_GB
dc.language.isoenen_GB
dc.publisherAssociation for Computational Linguisticsen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectGenetic algorithmsen_GB
dc.subjectGenetic programming (Computer science)en_GB
dc.subjectRule-based programmingen_GB
dc.subjectWeb-based instruction -- Designen_GB
dc.titleEvolutionary algorithms for definition extractionen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holderen_GB
dc.bibliographicCitation.conferencename1st Workshop on Definition Extractionen_GB
dc.bibliographicCitation.conferenceplaceBorovets, Bulgaria, 18/09/2009en_GB
dc.description.reviewedpeer-revieweden_GB
Appears in Collections:Scholarly Works - FacICTCS

Files in This Item:
File Description SizeFormat 
Evolutionary Algorithms for Definition Extraction.pdf627.87 kBAdobe PDFView/Open


Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.