Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/1519
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aras, Gizem | - |
dc.contributor.author | Makaroğlu, Didem | - |
dc.contributor.author | Demir, Şeniz | - |
dc.contributor.author | Çakır, Altan | - |
dc.date.accessioned | 2021-07-27T10:47:43Z | - |
dc.date.available | 2021-07-27T10:47:43Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://doi.org/10.1016/j.eswa.2021.115049 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/1519 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Named entity recognition | en_US |
dc.subject | Turkish | en_US |
dc.subject | Transfer learning | en_US |
dc.subject | CRF | en_US |
dc.subject | Digital media industry | en_US |
dc.title | An evaluation of recent neural sequence tagging models in Turkish named entity recognition | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.eswa.2021.115049 | - |
dc.identifier.scopus | 2-s2.0-85107884455 | en_US |
dc.authorid | Yazar ID | - |
dc.description.woscitationindex | Science Citation Index Expanded | - |
dc.identifier.wosquality | Q1 | - |
dc.description.WoSDocumentType | Article | |
dc.description.WoSInternationalCollaboration | Uluslararası işbirliği ile yapılmayan - HAYIR | en_US |
dc.description.WoSPublishedMonth | Kasım | en_US |
dc.description.WoSIndexDate | 2021 | en_US |
dc.description.WoSYOKperiod | YÖK - 2021-22 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.endpage | 11 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.identifier.issue | 15 | en_US |
dc.identifier.volume | 182 | en_US |
dc.department | Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.relation.journal | Expert Systems with Applications | en_US |
dc.identifier.wos | WOS:000688460900011 | en_US |
dc.institutionauthor | Demir, Şeniz | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairetype | Article | - |
crisitem.author.dept | 02.02. Department of Computer Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S0957417421004905-main.pdf | Full Text / Tam Metin | 1.17 MB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
20
checked on Aug 1, 2024
WEB OF SCIENCETM
Citations
13
checked on Jun 23, 2024
Page view(s)
4
checked on Jun 26, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.