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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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
Subjects:	P Language and Literature > P Philology. Linguistics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
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:	Date Event August 2018 Published 11 March 2018 Accepted
Date of first compliant deposit:	1 October 2018
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:	Project/Grant ID RIOXX Funder Name Funder ID 61528302 [NSFC] National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809 BK20161430 Natural Science Foundation of Jiangsu Province http://dx.doi.org/10.13039/501100004608 UNSPECIFIED Collaborative Innovation Centre of Wireless Communications Technology UNSPECIFIED

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