If You Even Don’t Have a Bit of Bible: Learning Delexicalized POS Taggers

Zhiwei Yu, David Mareček, Zdeněk Žabokrtský, Daniel Zeman


Abstract
Part-of-speech (POS) induction is one of the most popular tasks in research on unsupervised NLP. Various unsupervised and semi-supervised methods have been proposed to tag an unseen language. However, many of them require some partial understanding of the target language because they rely on dictionaries or parallel corpora such as the Bible. In this paper, we propose a different method named delexicalized tagging, for which we only need a raw corpus of the target language. We transfer tagging models trained on annotated corpora of one or more resource-rich languages. We employ language-independent features such as word length, frequency, neighborhood entropy, character classes (alphabetic vs. numeric vs. punctuation) etc. We demonstrate that such features can, to certain extent, serve as predictors of the part of speech, represented by the universal POS tag.
Anthology ID:
L16-1015
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
96–103
Language:
URL:
https://www.aclweb.org/anthology/L16-1015
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/L16-1015.pdf