{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T20:47:09Z","timestamp":1786999629009,"version":"build-2736575974"},"reference-count":45,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Nat. Lang. Eng."],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using any hand-crafted features and\/or domain-specific resources such as gazetteers and lexicons. In this regard, we utilize character-level, character n-gram-level, morpheme-level, and orthographic character-level word representations. Since noisy data with NER annotation are scarce for Turkish, we introduce a transfer learning model in order to learn infrequent entity types as an extension to the Bi-LSTM-CRF architecture by incorporating an additional conditional random field (CRF) layer that is trained on a larger (but formal) text and a noisy text simultaneously. This allows us to learn from both formal and informal\/noisy text, thus improving the performance of our model further for rarely seen entity types. We experimented on Turkish as a morphologically rich language and English as a relatively morphologically poor language. We obtained an entity-level F1 score of 67.39% on Turkish noisy data and 45.30% on English noisy data, which outperforms the current state-of-art models on noisy text. The English scores are lower compared to Turkish scores because of the intense sparsity in the data introduced by the user writing styles. The results prove that using subword information significantly contributes to learning latent features for morphologically rich languages.<\/jats:p>","DOI":"10.1017\/s1351324919000627","type":"journal-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T03:54:23Z","timestamp":1580183663000},"page":"35-64","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":16,"title":["Transfer learning for Turkish named entity recognition on noisy text"],"prefix":"10.1017","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0758-4355","authenticated-orcid":false,"given":"Emre","family":"Ka\u011fan Akkaya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1700-0395","authenticated-orcid":false,"given":"Burcu","family":"Can","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2020,1,28]]},"reference":[{"key":"S1351324919000627_ref9","doi-asserted-by":"crossref","unstructured":"Derczynski, L. , Nichols, E. , van Erp, M. and Limsopatham, N. (2017). Results of the WNUT2017 shared task on novel and emerging entity recognition. In Proceedings of the 3rd Workshop on Noisy User-generated Text, Copenhagen, Denmark. Association for Computational Linguistics, pp. 140\u2013147.","DOI":"10.18653\/v1\/W17-4418"},{"key":"S1351324919000627_ref40","unstructured":"\u00dcst\u00fcn, A. , Kurfal, M. and Can, B. (2018). Characters or morphemes: How to represent words? In Proceedings of The Third Workshop on Representation Learning for NLP, Melbourne, Australia. Association for Computational Linguistics, pp. 144\u2013153."},{"key":"S1351324919000627_ref10","doi-asserted-by":"publisher","DOI":"10.5121\/csit.2015.50213"},{"key":"S1351324919000627_ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4420"},{"key":"S1351324919000627_ref18","doi-asserted-by":"publisher","DOI":"10.1080\/01638539809545028"},{"key":"S1351324919000627_ref21","unstructured":"Ma, X. and Hovy, E. (2016). End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354."},{"key":"S1351324919000627_ref37","doi-asserted-by":"crossref","unstructured":"Toruno\u011flu, D. and Eryi\u011fit, G. (2014). A cascaded approach for social media text normalization of Turkish. In Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM), Gothenburg, Sweden. Association for Computational Linguistics, pp. 62\u201370.","DOI":"10.3115\/v1\/W14-1308"},{"key":"S1351324919000627_ref42","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4423"},{"key":"S1351324919000627_ref25","unstructured":"Okur, E. , Demir, H. and \u00d6zg\u00fcr, A. (2016). Named entity recognition on Twitter for Turkish using semi-supervised learning with word embeddings. In LREC."},{"key":"S1351324919000627_ref31","doi-asserted-by":"crossref","unstructured":"Sak, H. , G\u00fcng\u00f6r, T. and Sara\u00e7lar, M. (2008). Turkish language resources: Morphological parser, morphological disambiguator and web corpus. In Advances in Natural Language Processing, pp. 417\u2013427. Springer.","DOI":"10.1007\/978-3-540-85287-2_40"},{"key":"S1351324919000627_ref1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4419"},{"key":"S1351324919000627_ref28","doi-asserted-by":"publisher","DOI":"10.3115\/1073012.1073067"},{"key":"S1351324919000627_ref44","unstructured":"Yang, Z. , Salakhutdinov, R. and Cohen, W.W. (2017). Transfer learning for sequence tagging with hierarchical recurrent networks. arXiv preprint arXiv:1703.06345."},{"key":"S1351324919000627_ref13","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"S1351324919000627_ref5","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-1603"},{"key":"S1351324919000627_ref17","unstructured":"Lample, G. , Ballesteros, M. , Subramanian, S. , Kawakami, K. and Dyer, C. (2016). Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360."},{"key":"S1351324919000627_ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4322"},{"key":"S1351324919000627_ref30","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2020"},{"key":"S1351324919000627_ref29","unstructured":"Reimers, N. , Eckle-Kohler, J. , Schnober, C. , Kim, J. and Gurevych, I. (2014). 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