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Named-entity recognition in Turkish legal texts

Published online by Cambridge University Press:  11 July 2022

Can Çetindağ ,
Berkay Yazıcıoğlu  and
Aykut Koç
Can Çetindağ
Affiliation:
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
Berkay Yazıcıoğlu
Affiliation:
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
Aykut Koç*
Affiliation:
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey
*
*Corresponding author. E-mail: [email protected]
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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.

Keywords

NLP in lawNERTurkish NERComputational lawNamed-entity recognition
Type
Article
Information
Natural Language Engineering , Volume 29 , Issue 3 , May 2023 , pp. 615 - 642
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© The Author(s), 2022. Published by Cambridge University Press

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