Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags

Onur Güngör, Suzan Uskudarli, Tunga Güngör


Abstract
Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator’s performance is shown to be competitive.
Anthology ID:
C18-1177
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2082–2092
Language:
URL:
https://aclanthology.org/C18-1177
DOI:
Bibkey:
Cite (ACL):
Onur Güngör, Suzan Uskudarli, and Tunga Güngör. 2018. Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2082–2092, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags (Güngör et al., COLING 2018)
Copy Citation:
PDF:
https://aclanthology.org/C18-1177.pdf
Code
 onurgu/joint-ner-and-md-tagger