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

Author:

Tursun Eziz1,Ganguly Debasis2,Osman Turghun3,Yang Ya-Ting4,Abdukerim Ghalip3,Zhou Jun-Lin5,Liu Qun2

Affiliation:

1. 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

2. ADAPT Centre, School of Computing, Dublin City University, Ireland

3. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, University of Chinese Academy of Science, Urumqi, China

4. Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China

5. Xinjiang Branch of Chinese Academy of Science, Urumqi, China

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%.

Funder

China Scholarship Council

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference27 articles.

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