Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings

Eda Okur, Hakan Demir, Arzucan Özgür


Abstract
Recently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we focused on the Named Entity Recognition (NER) problem on informal text types for Turkish. We utilized a semi-supervised learning approach based on neural networks. We applied a fast unsupervised method for learning continuous representations of words in vector space. We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts. We evaluated our Turkish NER system on Twitter messages and achieved better F-score performances than the published results of previously proposed NER systems on Turkish tweets. Since we did not employ any language dependent features, we believe that our method can be easily adapted to microblog texts in other morphologically rich languages.
Anthology ID:
L16-1087
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
549–555
Language:
URL:
https://aclanthology.org/L16-1087
DOI:
Bibkey:
Cite (ACL):
Eda Okur, Hakan Demir, and Arzucan Özgür. 2016. Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 549–555, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings (Okur et al., LREC 2016)
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PDF:
https://aclanthology.org/L16-1087.pdf