Extension of Conventional Co-Training Learning Strategies to Three-View and Committee-Based Learning Strategies for Effective Automatic Sentence Segmentation

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dc.contributor.authorDalva, Dogan
dc.contributor.authorGüz, Ümit
dc.contributor.authorGürkan, Hakan
dc.date.accessioned2026-02-12T21:02:48Z
dc.date.available2026-02-12T21:02:48Z
dc.date.issued2018
dc.departmentBursa Teknik Üniversitesi
dc.description2018 IEEE Spoken Language Technology Workshop, SLT 2018 -- 2018-12-18 through 2018-12-21 -- Athens -- 145107
dc.description.abstractThe objective of this work is to develop effective multiview semi-supervised machine learning strategies for sentence boundary classification problem when only small sets of sentence boundary labeled data are available. We propose three-view and committee-based learning strategies incorporating with co-training algorithms with agreement, disagreement, and self-combined learning strategies using prosodic, lexical and morphological information. We compare experimental results of proposed three-view and committee-based learning strategies to other semi-supervised learning strategies in the literature namely, self-training and co-training with agreement, disagreement, and self-combined strategies. The experiment results show that sentence segmentation performance can be highly improved using multi-view learning strategies that we propose since data sets can be represented by three redundantly sufficient and disjoint feature sets. We show that the proposed strategies substantially improve the average performance when only a small set of manually labeled data is available for Turkish and English spoken languages, respectively. © 2018 IEEE.
dc.description.sponsorship(09A301, 14A201); (107E182, 111E228); J. William Fulbright College of Arts and Sciences, University of Arkansas; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK
dc.description.sponsorshipIEEE Signal Processing Society; The Institute of Electrical and Electronics Engineers
dc.identifier.doi10.1109/SLT.2018.8639533
dc.identifier.endpage755
dc.identifier.isbn9781538643341
dc.identifier.scopus2-s2.0-85063073665
dc.identifier.scopusqualityN/A
dc.identifier.startpage750
dc.identifier.urihttps://doi.org/10.1109/SLT.2018.8639533
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6531
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2018 IEEE Spoken Language Technology Workshop, SLT 2018 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsAll Open Access; Gold Open Access
dc.snmzKA_Scopus_20260212
dc.subjectBoosting
dc.subjectCo-Training
dc.subjectProsody
dc.subjectSemi-supervised learning
dc.subjectSentence Segmentation
dc.titleExtension of Conventional Co-Training Learning Strategies to Three-View and Committee-Based Learning Strategies for Effective Automatic Sentence Segmentation
dc.typeConference Object

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