Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages

José María Hoya Quecedo, Koppatz Maximilian, Roman Yangarber

Abstract
We consider the problem of disambiguating the lemma and part of speech of ambiguous words in morphologically rich languages. We propose a method for disambiguating ambiguous words in context, using a large un-annotated corpus of text, and a morphological analyser—with no manual disambiguation or data annotation. We assume that the morphological analyser produces multiple analyses for ambiguous words. The idea is to train recurrent neural networks on the output that the morphological analyser produces for unambiguous words. We present performance on POS and lemma disambiguation that reaches or surpasses the state of the art—including supervised models—using no manually annotated data. We evaluate the method on several morphologically rich languages.
Anthology ID:
2020.lrec-1.439
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3573–3582
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.439
DOI:
Bibkey:
Cite (ACL):
José María Hoya Quecedo, Koppatz Maximilian, and Roman Yangarber. 2020. Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 3573–3582, Marseille, France. European Language Resources Association.
Cite (Informal):
Neural Disambiguation of Lemma and Part of Speech in Morphologically Rich Languages (Hoya Quecedo et al., LREC 2020)
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