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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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