Dependency parsing with stacked conditional random fields for Turkish

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dc.contributor.authorBilgin, Metin
dc.contributor.authorAmasyali, Mehmet Fatih
dc.date.accessioned2026-02-12T21:02:51Z
dc.date.available2026-02-12T21:02:51Z
dc.date.issued2017
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn the most general form Sequence Labelling is the production of an output sequence in response to an input sequence. Many of natural language processing problems such as (entity name recognition, machine translation, morphological analysis, separation of the elements of sentence etc.) can be defined as a sequence labelling. Dependency parsing is to determine the relationship and the type of the relationship between words within a sentence and it is essential to perform semantic analysis of a sentence. When dependency parsing is defined as a sequence labelling problem, production of two outputs (relationship type, related words) is required. Our recommendation is to use the Conditional Random Fields (CRF) which is commonly used in sequence labelling problems. However CRF is a method that produces a single output. To overcome this difficulty we propose to divide Dependency Parsing which is a problem with two outputs into two parts. The overall solution is provided by combining the results of these parts. With the performed operation we reached the best dependency parsing results for Turkish language.
dc.identifier.doi10.17341/gazimmfd.322162
dc.identifier.endpage392
dc.identifier.issn1300-1884
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85020547967
dc.identifier.scopusqualityQ2
dc.identifier.startpage385
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.322162
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6586
dc.identifier.volume32
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherGazi Universitesi Muhendislik-Mimarlik mmfd@gazi.edu.tr
dc.relation.ispartofJournal of the Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAll Open Access; Bronze Open Access
dc.snmzKA_Scopus_20260212
dc.subjectCondition random fields
dc.subjectDependency parsing
dc.subjectMachine learning
dc.subjectNatural language processing
dc.subjectOptimization
dc.subjectSequence labelling
dc.titleDependency parsing with stacked conditional random fields for Turkish
dc.title.alternativeTürkçe için ardişik şartli rastgele alanlarla bağlilik ayriştirma
dc.typeArticle

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