@inproceedings{mayhew-etal-2017-cheap,
title = "Cheap Translation for Cross-Lingual Named Entity Recognition",
author = "Mayhew, Stephen and
Tsai, Chen-Tse and
Roth, Dan",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D17-1269/",
doi = "10.18653/v1/D17-1269",
pages = "2536--2545",
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 \textit{very} minimal resources. Our approach makes use of a lexicon to {\textquotedblleft}translate{\textquotedblright} 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."
}
Markdown (Informal)
[Cheap Translation for Cross-Lingual Named Entity Recognition](https://preview.aclanthology.org/jlcl-multiple-ingestion/D17-1269/) (Mayhew et al., EMNLP 2017)
ACL