Skip to main content
SpringerLink
Log in

A Statistical Approach to Analyzing Turkish Morphology

  • Conference paper
  • First Online:
Advances in Automation, Mechanical and Design Engineering (SAMDE 2021)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 121))

  • 183 Accesses

Abstract

Morphological analyzers are essential components of Turkish language processing pipelines due to complex and rich morphology of Turkish language. Previous work usually focuses on two-level description of Turkish morphology, thus leads to lexicon and two-level rules oriented FST implementations. However, two-level based implementations are not robust to spelling errors and new loan words entering the Turkish lexicon. In this paper, we introduce a statistical approach to analyzing Turkish word forms by training and comparing two seq2seq models on our annotated dataset. We approach analyzing Turkish word forms as a machine translation problem, where the source sequence is a Turkish word form and the target sequence is a sequence of morphemes. Evaluating on three testsets of informal written language word forms, we show that our approach offers a robust approach to analyzing the Turkish words and proposes a strong baseline for sequential modeling of Turkish morphology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.dumps.wikimedia.org/trwiki/

  2. 2.

    https://www.github.com/turkish-nlp-suite/turkish-morph-lexicon.

  3. 3.

    Implementations can be found at https://www.github.com/turkish-nlp-suite/turkish-morph-lexicon/tree/main/keras_code

  4. 4.

    https://www.github.com/turkish-nlp-suite/turkish-morph-lexicon/blob/main/tagset.md.

References

  1. Oflazer, K., Gocmen, E., Bozsahin, C.: An Outline of Turkish Morphology (1994)

    Google Scholar 

  2. Oflazer, K., Saraçlar, M.: Turkish Natural Language Processing. Springer International Publishing (2018). https://www.books.google.de/books?id=D-5lDwAAQBAJ

  3. Chiu, J.P.C., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016). https://doi.org/10.1162/tacl_a_00104

    Article  Google Scholar 

  4. Koskenniemi, K.: Two-Level Model for Morphological Analysis. In: IJCAI (1983), pp. 683–685

    Google Scholar 

  5. Oflazer, .: Two-level Description of Turkish Morphology. Utrecht, The Netherlands, (1993). https://www.aclweb.org/anthology/E93-1066

  6. Sak, H., Güngör, T., Saraçlar, M.: A stochastic finite-state morphological parser for Turkish. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, USA, 2009, pp. 273–276

    Google Scholar 

  7. Çöltekin, C.: A freely available morphological analyzer for Turkish. Valletta, Malta (2010). https://www.lrec-conf.org/proceedings/lrec2010/pdf/109_Paper.pdf

  8. Akın, A.A., Akın, M.D.: Zemberek, an open source NLP framework for Turkic Languages (2007)

    Google Scholar 

  9. Ozturel, A., Kayadelen, T., Demirsahin, I.: A syntactically expressive morphological analyzer for Turkish. In: Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing, Dresden, Germany (2019), pp. 65–75. https://doi.org/10.18653/v1/W19-3110

  10. Stahlberg, F.: Neural machine translation: a review. CoRR (2019). http://www.arxiv.org/abs/1912.02047

  11. Tan, Z., et al.: Neural machine translation: a review of methods, resources, and tools. CoRR (2020). https://www.arxiv.org/abs/2012.15515

  12. Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. CoRR (2018). http://www.arxiv.org/abs/1812.09449

  13. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. CoRR (2014). http://www.arxiv.org/abs/1409.3215

  14. Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabulary speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4945–4949 (2016). https://doi.org/10.1109/ICASSP.2016.7472618

  15. Chiu, C.-C., et al.: State-of-the-art speech recognition with sequence-to-sequence models. CoRR (2017). http://www.arxiv.org/abs/1712.01769

  16. Nassif, A.B., Shahin, I., Attili, I., Azzeh, M., Shaalan, K.: Speech recognition using deep neural networks: a systematic review. IEEE Access 7, 19143–19165 (2019). https://doi.org/10.1109/ACCESS.2019.2896880

    Article  Google Scholar 

  17. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR (2015)

    Google Scholar 

  18. Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. CoRR (2015). http://www.arxiv.org/abs/1508.04025

  19. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  20. Göksel, A., Kerslake, C.: Turkish: A Comprehensive Grammar. Routledge (2005). https://www.books.google.de/books?id=7fXCKZmee8QC

  21. Petrov, S., Das, D., McDonald, R.: A universal part-of-speech tagset. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey, May 2012, pp. 2089–2096. https://www.lrec-conf.org/proceedings/lrec2012/pdf/274_Paper.pdf

  22. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, June 2016, pp. 1480–1489. https://doi.org/10.18653/v1/N16-1174

  23. Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization (2017)

    Google Scholar 

  24. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy, May 2010, vol. 9, pp. 249–256. http://www.proceedings.mlr.press/v9/glorot10a.html

Download references

Author information

Authors and Affiliations

  1. Deepgram San Francisco, San Francisco, CA, 94104, US

    Duygu Altinok

Authors
  1. Duygu Altinok
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duygu Altinok .

Editor information

Editors and Affiliations

  1. SP2MI—Site du Futuroscope, University of Poitiers, Poitiers, France

    Med Amine Laribi

  2. DIMEG, University of Calabria, Rende, Italy

    Giuseppe Carbone

  3. iOPEN, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Zhiyu Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Altinok, D. (2023). A Statistical Approach to Analyzing Turkish Morphology. In: Laribi, M.A., Carbone, G., Jiang, Z. (eds) Advances in Automation, Mechanical and Design Engineering. SAMDE 2021. Mechanisms and Machine Science, vol 121. Springer, Cham. https://doi.org/10.1007/978-3-031-09909-0_12

Download citation

Publish with us

Policies and ethics

Search

Navigation