Named-entity recognition in Turkish legal texts

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buir.contributor.authorÇetindağ, , Can
buir.contributor.authorYazıcıoğlu, Berkay
buir.contributor.authorKoç, Aykut
dc.citation.epage28en_US
dc.citation.spage1en_US
dc.contributor.authorÇetindağ, Can
dc.contributor.authorYazıcıoğlu, Berkay
dc.contributor.authorKoç, Aykut
dc.date.accessioned2023-02-16T07:20:01Z
dc.date.available2023-02-16T07:20:01Z
dc.date.issued2022-07-11
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.description.abstractNatural language processing (NLP) technologies and applications in legal text processing are gaining momentum. Being one of the most prominent tasks in NLP, named-entity recognition (NER) can substantiate a great convenience for NLP in law due to the variety of named entities in the legal domain and their accentuated importance in legal documents. However, domain-specific NER models in the legal domain are not well studied. We present a NER model for Turkish legal texts with a custom-made corpus as well as several NER architectures based on conditional random fields and bidirectional long-short-term memories (BiLSTMs) to address the task. We also study several combinations of different word embeddings consisting of GloVe, Morph2Vec, and neural network-based character feature extraction techniques either with BiLSTM or convolutional neural networks. We report 92.27% F1 score with a hybrid word representation of GloVe and Morph2Vec with character-level features extracted with BiLSTM. Being an agglutinative language, the morphological structure of Turkish is also considered. To the best of our knowledge, our work is the first legal domain-specific NER study in Turkish and also the first study for an agglutinative language in the legal domain. Thus, our work can also have implications beyond the Turkish language.en_US
dc.description.provenanceSubmitted by İrem Aro (iremaro18@gmail.com) on 2023-02-16T07:20:01Z No. of bitstreams: 1 Named-entity_recognition_in_Turkish_legal_texts.pdf: 1039499 bytes, checksum: d1d0996d38706bbb6f95beaa25c584ca (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-16T07:20:01Z (GMT). No. of bitstreams: 1 Named-entity_recognition_in_Turkish_legal_texts.pdf: 1039499 bytes, checksum: d1d0996d38706bbb6f95beaa25c584ca (MD5) Previous issue date: 2022-07-11en
dc.identifier.doi10.1017/S1351324922000304en_US
dc.identifier.eissn1469-8110
dc.identifier.issn1351-3249
dc.identifier.urihttp://hdl.handle.net/11693/111393
dc.language.isoEnglishen_US
dc.publisherCambridge University Pressen_US
dc.relation.isversionofhttp://dx.doi.org/10.1017/S1351324922000304en_US
dc.source.titleNatural Language Engineeringen_US
dc.subjectNLP in lawen_US
dc.subjectNERen_US
dc.subjectTurkish NERen_US
dc.subjectComputational lawen_US
dc.subjectNamed-entity recognitionen_US
dc.titleNamed-entity recognition in Turkish legal textsen_US
dc.typeArticleen_US

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