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Weakly supervised POS tagging without disambiguation
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Zhou, Deyu, Zhang, Zhikai, Zhang, Min-Ling and He, Yulan (2018) Weakly supervised POS tagging without disambiguation. ACM Transactions on Asian and Low-Resource Language Information Processing, 17 (4). 35. doi:10.1145/3214707
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Official URL: http://dx.doi.org/10.1145/3214707
Abstract
Weakly supervised part-of-speech (POS) tagging is to learn to predict the POS tag for a given word in context by making use of partial annotated data instead of the fully tagged corpora. Weakly supervised POS tagging would benefit various natural language processing applications in such languages where tagged corpora are mostly unavailable.
In this article, we propose a novel framework for weakly supervised POS tagging based on a dictionary of words with their possible POS tags. In the constrained error-correcting output codes (ECOC)-based approach, a unique L-bit vector is assigned to each POS tag. The set of bitvectors is referred to as a coding matrix with value { 1, -1}. Each column of the coding matrix specifies a dichotomy over the tag space to learn a binary classifier. For each binary classifier, its training data is generated in the following way: each pair of words and its possible POS tags are considered as a positive training example only if the whole set of its possible tags falls into the positive dichotomy specified by the column coding and similarly for negative training examples. Given a word in context, its POS tag is predicted by concatenating the predictive outputs of the L binary classifiers and choosing the tag with the closest distance according to some measure. By incorporating the ECOC strategy, the set of all possible tags for each word is treated as an entirety without the need of performing disambiguation. Moreover, instead of manual feature engineering employed in most previous POS tagging approaches, features for training and testing in the proposed framework are automatically generated using neural language modeling. The proposed framework has been evaluated on three corpora for English, Italian, and Malagasy POS tagging, achieving accuracies of 93.21%, 90.9%, and 84.5% individually, which shows a significant improvement compared to the state-of-the-art approaches.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science > Computer Science | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Natural language processing (Computer science), Error-correcting codes (Information theory), Parts of speech, Corpora (Linguistics) | ||||||||||||
| Journal or Publication Title: | ACM Transactions on Asian and Low-Resource Language Information Processing | ||||||||||||
| Publisher: | ACM | ||||||||||||
| ISSN: | 2375-4699 | ||||||||||||
| Official Date: | August 2018 | ||||||||||||
| Dates: |
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| Volume: | 17 | ||||||||||||
| Number: | 4 | ||||||||||||
| Article Number: | 35 | ||||||||||||
| DOI: | 10.1145/3214707 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publisher Statement: | © ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Asian and Low-Resource Language Information Processing, http://dx.doi.org/10.1145/10.1145/3214707 | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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