NDLI: A Semisupervised Tag-Transition-Based Markovian Model for Uyghur Morphology Analysis

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A Semisupervised Tag-Transition-Based Markovian Model for Uyghur Morphology Analysis

Content Provider	ACM Digital Library
Author	Ganguly, Debasis Liu, Qun Tursun, Eziz Yang, Ya-Ting Abdukerim, Ghalip Osman, Turghun Zhou, Jun-Lin
Copyright Year	2016
Description	Author Affiliation: Xinjiang Branch of Chinese Academy of Science, Urumqi, China(Xinjiang technical institute of physics and chemistry, Chinese Academy of Science, Urumqi, China (Yang, Ya-Ting; Xinjiang technical institute of physics and chemistry, Chinese Academy of Science, University of Chinese Academy of Science, Institute of Mathematics and Information of Hotan Teachers College, Urumqi, China (Tursun, Eziz); Liu, Qun); Adapt centre, School of Computing, Dublin City University, Ireland (Ganguly, Debasis; Abdukerim, Ghalip); Xinjiang technical institute of physics and chemistry, Chinese Academy of Science, University of Chinese Academy of Science, Urumqi, China (Osman, Turghun; Zhou, Jun-Lin))
Abstract	Morphological analysis, which includes analysis of part-of-speech (POS) tagging, stemming, and morpheme segmentation, is one of the key components in natural language processing (NLP), particularly for agglutinative languages. In this article, we investigate the morphological analysis of the Uyghur language, which is the native language of the people in the Xinjiang Uyghur autonomous region of western China. Morphological analysis of Uyghur is challenging primarily because of factors such as (1) ambiguities arising due to the likelihood of association of a multiple number of POS tags with a word stem or a multiple number of functional tags with a word suffix, (2) ambiguous morpheme boundaries, and (3) complex morphopholonogy of the language. Further, the unavailability of a manually annotated training set in the Uyghur language for the purpose of word segmentation makes Uyghur morphological analysis more difficult. In our proposed work, we address these challenges by undertaking a semisupervised approach of learning a Markov model with the help of a manually constructed dictionary of “suffix to tag” mappings in order to predict the most likely tag transitions in the Uyghur morpheme sequence. Due to the linguistic characteristics of Uyghur, we incorporate a prior belief in our model for favoring word segmentations with a lower number of morpheme units. Empirical evaluation of our proposed model shows an accuracy of about 82%. We further improve the effectiveness of the tag transition model with an active learning paradigm. In particular, we manually investigated a subset of words for which the model prediction ambiguity was within the top 20%. Manually incorporating rules to handle these erroneous cases resulted in an overall accuracy of 93.81%.
Starting Page	1
Ending Page	23
Page Count	23
File Format	PDF
ISSN	23754699
e-ISSN	23754702
DOI	10.1145/2968410
Volume Number	16
Issue Number	2
Journal	ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Markov model Uyghur morphological analysis
Content Type	Text
Resource Type	Article
Subject	Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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