Advertisement

Turkish Named-Entity Recognition

  • Reyyan Yeniterzi
  • Gökhan Tür
  • Kemal OflazerEmail author
Chapter
Part of the Theory and Applications of Natural Language Processing book series (NLP)

Abstract

Named-entity recognition is an important task for many other natural language processing tasks and applications such as information extraction, question answering, sentiment analysis, machine translation, etc. Over the last decades named-entity recognition for Turkish has attracted significant attention both in terms of systems development and resource development. After a brief description of the general named-entity recognition task, this chapter presents a comprehensive overview of the work on Turkish named-entity recognition along with the data resources various research efforts have built.

References

  1. Bayraktar Ö, Temizel TT (2008) Person name extraction from Turkish financial news text using local grammar based approach. In: Proceedings of ISCIS, IstanbulGoogle Scholar
  2. Çelikkaya G, Torunoğlu D, Eryiğit G (2013) Named entity recognition on real data: a preliminary investigation for Turkish. In: Proceedings of the international conference on application of information and communication technologies, BakuGoogle Scholar
  3. Chinchor N, Marsh E (1998) Appendix D: MUC-7 information extraction task definition (version 5.1). In: Proceedings of MUC, Fairfax, VAGoogle Scholar
  4. Collobert R, Weston J, Bottou L, Karlen M, Kavukçuoğlu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537Google Scholar
  5. Cucerzan S, Yarowsky D (1999) Language independent named entity recognition combining morphological and contextual evidence. In: Proceedings of EMNLP-VLC, College Park, MD, pp 90–99Google Scholar
  6. Dalkılıç FE, Gelişli S, Diri B (2010) Named entity recognition from Turkish texts. In: Proceedings of IEEE signal processing and communications applications conference, Diyarbakır, pp 918–920Google Scholar
  7. Demir H, Özgür A (2014) Improving named entity recognition for morphologically rich languages using word embeddings. In: Proceedings of the international conference on machine learning and applications, Detroit, MI, pp 117–122Google Scholar
  8. Doddington G, Mitchell A, Przybocki M, Ramshaw L, Strassel S, Weischedel R (2004) The automatic content extraction (ACE) program–tasks, data, and evaluation. In: Proceedings of LREC, Lisbon, pp 837–840Google Scholar
  9. Eken B, Tantuğ C (2015) Recognizing named-entities in Turkish tweets. In: Proceedings of the international conference on software engineering and applications, DubaiGoogle Scholar
  10. Freitag D (2000) Machine learning for information extraction in informal domains. Mach Learn 39(2–3):169–202Google Scholar
  11. Kısa KD, Karagöz P (2015) Named entity recognition from scratch on social media. In: Proceedings of the international workshop on mining ubiquitous and social environments, PortoGoogle Scholar
  12. Küçük D, Steinberger R (2014) Experiments to improve named entity recognition on Turkish tweets. Arxiv – computing research repository. arxiv.org/abs/1410.8668. Accessed 14 Sept 2017
  13. Küçük D, Yazıcı A (2009a) Named entity recognition experiments on Turkish texts. In: Proceedings of the international conference on flexible query answering systems, Roskilde, pp 524–535Google Scholar
  14. Küçük D, Yazıcı A (2009b) Rule-based named entity recognition from Turkish texts. In: Proceedings of the international symposium on innovations in intelligent systems and applications, TrabzonGoogle Scholar
  15. Küçük D, Yazıcı A (2010) A hybrid named entity recognizer for Turkish with applications to different text genres. In: Proceedings of ISCIS, London, pp 113–116Google Scholar
  16. Küçük D, Yazıcı A (2012) A hybrid named entity recognizer for Turkish. Expert Syst Appl 39(3):2733–2742Google Scholar
  17. Küçük D, Jacquet G, Steinberger R (2014) Named entity recognition on Turkish tweets. In: Proceedings of LREC, Reykjavík, pp 450–454Google Scholar
  18. Lafferty JD, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML, Williams, MA, pp 282–289Google Scholar
  19. Mason O (2004) Automatic processing of local grammar patterns. In: Proceedings of the annual colloquium for the UK special interest group for computational linguistics, Birmingham, pp 166–171Google Scholar
  20. Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1):3–26Google Scholar
  21. Oflazer K (1994) Two-level description of Turkish morphology. Lit Linguist Comput 9(2):137–148Google Scholar
  22. Önal KD, Karagöz P, Çakıcı R (2014) Toponym recognition on Turkish tweets. In: Proceedings of IEEE signal processing and communications applications conference, Trabzon, pp 1758–1761Google Scholar
  23. Özkaya S, Diri B (2011) Named entity recognition by conditional random fields from Turkish informal texts. In: Proceedings of IEEE signal processing and communications applications conference, Antalya, pp 662–665Google Scholar
  24. Pouliquen B, Steinberger R (2009) Automatic construction of multilingual name dictionaries. In: Goutte C, Cancedda N, Dymetman M, Foster G (eds) Learning machine translation. The MIT Press, Cambridge, MA, pp 266–290Google Scholar
  25. Ramshaw LA, Marcus MP (1995) Text chunking using transformation-based learning. In: Proceedings of the workshop on very large corpora, Cambridge, MA, pp 82–94Google Scholar
  26. Ratinov L, Roth D (2009) Design challenges and misconceptions in named entity recognition. In: Proceedings of CONLL, Boulder, CO, pp 147–155Google Scholar
  27. Ritter A, Clark S, Mausam, Etzioni O (2011) Named entity recognition in tweets: an experimental study. In: Proceedings of EMNLP, Edinburgh, pp 1524–1534Google Scholar
  28. Sak H, Güngör T, Saraçlar M (2011) Resources for Turkish morphological processing. Lang Resour Eval 45(2):249–261Google Scholar
  29. Say B, Zeyrek D, Oflazer K, Özge U (2004) Development of a corpus and a treebank for present-day written Turkish. In: Proceedings of the international conference on Turkish linguistics, Magosa, pp 183–192Google Scholar
  30. Şeker GA, Eryiğit G (2012) Initial explorations on using CRFs for Turkish named entity recognition. In: Proceedings of COLING, Mumbai, pp 2459–2474Google Scholar
  31. Sha F, Pereira F (2003) Shallow parsing with conditional random fields. In: Proceedings of NAACL-HLT, Edmonton, pp 134–141Google Scholar
  32. Sundheim BM (1995) Overview of results of the MUC-6 evaluation. In: Proceedings of MUC, Columbia, MD, pp 13–31Google Scholar
  33. Tatar S, Çiçekli İ (2011) Automatic rule learning exploiting morphological features for named entity recognition in Turkish. J Inf Sci 37(2):137–151Google Scholar
  34. Tjong Kim Sang EF (2002) Introduction to the CoNLL-2002 shared task: language-independent named entity recognition. In: Proceedings of CONNL, Taipei, pp 1–4Google Scholar
  35. Tjong Kim Sang EF, De Meulder F (2003) Introduction to the CoNLL-2003 Shared Task: language-independent named entity recognition. In: Proceedings of CONLL, Edmonton, pp 142–147Google Scholar
  36. Traboulsi HN (2006) Named entity recognition: a local grammar-based approach. PhD thesis, Surrey University, GuildfordGoogle Scholar
  37. Tür G (2000) A statistical information extraction system for Turkish. PhD thesis, Bilkent University, AnkaraGoogle Scholar
  38. Tür G, Hakkani-Tür DZ, Oflazer K (2003) A statistical information extraction system for Turkish. Nat Lang Eng 9:181–210Google Scholar
  39. Yavuz SR, Küçük D, Yazıcı A (2013) Named entity recognition in Turkish with Bayesian learning and hybrid approaches. In: Proceedings of ISCIS, Paris, pp 129–138Google Scholar
  40. Yeniterzi R (2011) Exploiting morphology in Turkish named entity recognition system. In: Proceedings of ACL-HLT, Portland, OR, pp 105–110Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Özyeǧin UniversityIstanbulTurkey
  2. 2.Google ResearchMountain ViewUSA
  3. 3.Carnegie Mellon University QatarDoha-Education CityQatar

Personalised recommendations