Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1519
Title: An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition
Authors: Makaroğlu, Didem
Demir, Şeniz
Aras, Gizem
Çakır, Altan
Keywords: Turkish
Named entity recognition
Transfer learning
Digital media industry
CRF
Publisher: Elsevier
Abstract: Named entity recognition (NER) is an extensively studied task that extracts and classifies named entities in a text. NER is crucial not only in downstream language processing applications such as relation extraction and question answering but also in large scale big data operations such as real-time analysis of online digital media content. Recent research efforts on Turkish, a less studied language with morphologically rich nature, have demonstrated the effectiveness of neural architectures on well-formed texts and yielded state-of-the art results by formulating the task as a sequence tagging problem. In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory (BiLSTM) and Transformer-based networks) proposed for Turkish NER tagging in the same setting. Our results demonstrate that transformer-based networks which can model long-range context overcome the limitations of BiLSTM networks where different input features at the character, subword, and word levels are utilized. We also propose a transformer-based network with a conditional random field (CRF) layer that leads to the state-of-the-art result (95.95% f-measure) on a common dataset. Our study contributes to the literature that quantifies the impact of transfer learning on processing morphologically rich languages.
URI: https://hdl.handle.net/20.500.11779/1519
https://doi.org/10.1016/j.eswa.2021.115049
ISSN: 0957-4174
Appears in Collections:Bilgisayar Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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