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- W4285014497 abstract "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." @default.
- W4285014497 created "2022-07-12" @default.
- W4285014497 creator A5018907760 @default.
- W4285014497 creator A5062231547 @default.
- W4285014497 creator A5073444375 @default.
- W4285014497 date "2022-07-11" @default.
- W4285014497 modified "2023-09-26" @default.
- W4285014497 title "Named-entity recognition in Turkish legal texts" @default.
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- W4285014497 doi "https://doi.org/10.1017/s1351324922000304" @default.
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