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
- 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)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/D17-1269.pdf
- Data
- Panlex