Cheap Translation for Cross-Lingual Named Entity Recognition

Stephen Mayhew, Chen-Tse Tsai, Dan Roth


Abstract
Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e.g., the availability of Wikipedia in the target language. We propose a simple method for cross-lingual named entity recognition (NER) that works well in settings with very minimal resources. Our approach makes use of a lexicon to “translate” annotated data available in one or several high resource language(s) into the target language, and learns a standard monolingual NER model there. Further, when Wikipedia is available in the target language, our method can enhance Wikipedia based methods to yield state-of-the-art NER results; we evaluate on 7 diverse languages, improving the state-of-the-art by an average of 5.5% F1 points. With the minimal resources required, this is an extremely portable cross-lingual NER approach, as illustrated using a truly low-resource language, Uyghur.
Anthology ID:
D17-1269
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2536–2545
Language:
URL:
https://aclanthology.org/D17-1269
DOI:
10.18653/v1/D17-1269
Bibkey:
Cite (ACL):
Stephen Mayhew, Chen-Tse Tsai, and Dan Roth. 2017. Cheap Translation for Cross-Lingual Named Entity Recognition. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2536–2545, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Cheap Translation for Cross-Lingual Named Entity Recognition (Mayhew et al., EMNLP 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/D17-1269.pdf
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