Named-entity recognition in Turkish legal texts
buir.contributor.author | Çetindağ, , Can | |
buir.contributor.author | Yazıcıoğlu, Berkay | |
buir.contributor.author | Koç, Aykut | |
dc.citation.epage | 28 | en_US |
dc.citation.spage | 1 | en_US |
dc.contributor.author | Çetindağ, Can | |
dc.contributor.author | Yazıcıoğlu, Berkay | |
dc.contributor.author | Koç, Aykut | |
dc.date.accessioned | 2023-02-16T07:20:01Z | |
dc.date.available | 2023-02-16T07:20:01Z | |
dc.date.issued | 2022-07-11 | |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.department | National Magnetic Resonance Research Center (UMRAM) | en_US |
dc.description.abstract | Natural 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.provenance | Submitted 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.provenance | Made 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: 2022-07-11 | en |
dc.identifier.doi | 10.1017/S1351324922000304 | en_US |
dc.identifier.eissn | 1469-8110 | |
dc.identifier.issn | 1351-3249 | |
dc.identifier.uri | http://hdl.handle.net/11693/111393 | |
dc.language.iso | English | en_US |
dc.publisher | Cambridge University Press | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1017/S1351324922000304 | en_US |
dc.source.title | Natural Language Engineering | en_US |
dc.subject | NLP in law | en_US |
dc.subject | NER | en_US |
dc.subject | Turkish NER | en_US |
dc.subject | Computational law | en_US |
dc.subject | Named-entity recognition | en_US |
dc.title | Named-entity recognition in Turkish legal texts | en_US |
dc.type | Article | en_US |
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