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TMD-NER: Turkish multi-domain named entity recognition for informal texts

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Abstract

We examine named entity recognition (NER), an essential and commonly used first step in many natural language processing tasks, including chatbots and language translation. We focus on the application of NER to texts that have a lot of noise, such as tweets, which is difficult due to the casual and unstructured language often used in these mediums. In this study, we make use of the largest available labeled data sets for Turkish NER, specifically targeting three informal platforms, namely Twitter, Facebook and Donanimhaber. We choose Turkish as a representative agglutinative language, which has a significantly different structure than other well-known languages such as English, French, and German. We emphasize that the methodologies and insights gained from this study can be extended to other agglutinative languages, like Finnish, Hungarian, Japanese, and Korean. We apply NER to these datasets using 16 different named entity tags through a framework that employs bidirectional long short-term memory (BiLSTM) networks followed by conditional random fields (CRF), known together as the BiLSTM-CRF model. Our experiments show an F1 score of 84% on a combined dataset, which indicates that deep learning models can also be effectively used for business applications in informal settings in agglutinative languages such as Turkish.

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Data sharing is not applicable to this article as no new data were analyzed in this study.

Notes

  1. For instance, employing i as opposed to ı, or g instead of ğ.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Selim F. Yilmaz

  2. Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey

    Furkan B. Mutlu & Suleyman S. Kozat

  3. Department of Statistics, Middle East Technical University, Ankara, Turkey

    Ismail Balaban

Authors
  1. Selim F. Yilmaz
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  2. Furkan B. Mutlu
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  3. Ismail Balaban
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  4. Suleyman S. Kozat
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All authors agreed on the content of this study. SY and FM conducted the analysis based on the agreed steps. Results and conclusions are examined and written together.

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Correspondence to Furkan B. Mutlu.

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This work has appeared in part at the 2022 IEEE Signal Processing and Communications Applications Conference [1] and was done when Selim F. Yilmaz was affiliated with Bilkent University, Ankara, Turkey.

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Yilmaz, S.F., Mutlu, F.B., Balaban, I. et al. TMD-NER: Turkish multi-domain named entity recognition for informal texts. SIViP 18, 2255–2263 (2024). https://doi.org/10.1007/s11760-023-02898-0

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