Uyghur morphological analysis using joint conditional random fields: Based on small scaled corpus

Ghalip Abdukerim; Eziz Tursun; Yating Yang; Xiao Li

doi:10.3934/dcdss.2019055

Article Contents

2019, Volume 12, Issue 4&5: 823-836. Doi: 10.3934/dcdss.2019055 open access

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Uyghur morphological analysis using joint conditional random fields: Based on small scaled corpus

1.
Xinjiang Technical Institute of Physical and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Institute of Mathematics and Information of Hotan Teachers College, Hotan 848000, China

* Corresponding author: Ghalip Abdukerim
* Corresponding author: Ghalip Abdukerim

Received: May 31, 2017

Revised: September 30, 2017

Published: March 2019

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Abstract

As a fundamental research in the field of natural language processing, the Uyghur morphological analysis is used mainly to determine the part of speech (POS) and segmental morphemes (stem and affix) of a word in a given sentence, as well as to automatically annotate the grammatical function of the morphemes based on the context. It is necessary to provide various information for other tasks of natural language processing including syntactic analysis, machine translation, automatic summarization, and semantic analysis, etc. In order to increase the morphological analysis efficiency, this paper puts forward a hybrid approach to create a statistical model for Uyghur morphological tagging through a small-scale corpus. Experimental results show that this plan can obtain an overall accuracy of 92.58 % with a limited training corpus.

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Mathematics Subject Classification: Primary: 68T50, 68U15; Secondary: 60G60.

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Figure 1. The morphological analysis result and hierarchical relationship of a Uyghur sentence

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Figure 2. The Architecture of a semi-supervised morphological analysis based on the hybrid approach

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Figure 3. Morphological Tag Decoding Process of Words in the Sentence

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Figure 4. The Relationship between Parameter $\beta$ and Accuracy

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Table 1. Feature Template of POS Tagging Model

Features	Description
${{w}_{i-2}}{{pos}_{i}}$, ${{w}_{i-1}}{{pos}_{i}}$,
${{w}_{i}}{{pos}_{i}}$, ${{w}_{i+1}}{{pos}_{i}}$,
${{w}_{i+2}}{{pos}_{i}}$	Unary context features of the word
${{w}_{i-2}}{{w}_{i-1}}{{pos}_{i}}$, ${{w}_{i-1}}{{w}_{i}}{{pos}_{i}}$,
${{w}_{i}}{{w}_{i+1}}{{pos}_{i}}$, ${{w}_{i+1}}{{w}_{i+2}}{{pos}_{i}}$,
${{w}_{i-1}}{{w}_{i+1}}{{pos}_{i}}$	Binary context features of the word
$h_1(w_i){{pos}_{i}}$, $h_2(w_i){{pos}_{i}}$,
$h_3(w_i){{pos}_{i}}$,
$h_4(w_i){{pos}_{i}}$,
$h_5(w_i){{pos}_{i}}$	n characters selected from the beginning of the word
$t_1(w_i){{pos}_{i}}$, $t_2(w_i){{pos}_{i}}$, $t_3(w_i){{pos}_{i}}$,
$t_4(w_i){{pos}_{i}}$, $t_5(w_i){{pos}_{i}}$	n characters selected from the end of the word
${{pos}_{i-1}}{{pos}_{i}}$	POS tag transition feature

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Table 2. Feature Template of the Morphological Tagging Model

Features	Description
${{m}_{i-2}}{{t}_{i}}$, ${{m}_{i-1}}{{t}_{i}}$, ${{m}_{i}}{{t}_{i}}$, ${{m}_{i+1}}{{t}_{i}}$, ${{m}_{i+2}}{{t}_{i}}$	Unary context features of the morpheme
${{m}_{i-2}}{{m}_{i-1}}{{t}_{i}}$, ${{m}_{i-1}}{{m}_{i}}{{t}_{i}}$, ${{m}_{i}}{{m}_{i+1}}{{t}_{i}}$,
${{m}_{i+1}}{{m}_{i+2}}{{t}_{i}}$, ${{m}_{i-1}}{{m}_{i+1}}{{t}_{i}}$	Binary context features of the morpheme
${{t}_{i-1}}{{t}_{i}}$	Morphological tag transition feature

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Table 3. List of Morphological Tag Candidates of Words in the Sentence

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Table 4. Manually Tagged Corpus Format and Content Example

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Table 5. Details of Experimental Data

	Number of sentences	Number of words (including punctuation marks)	Number of Uyghur words
Training set	1000	12433	10391
Development set	200	2564	2151
Test set	200	2492	2075

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Table 6. Experimental Results

Method	Accuracy (%)
Method	Stemming	Morpheme segmentation	POS	Overall
Tag sequence Markov model	90.18	83.25	86.17	75.13
Joint CRF model	91.98	85.79	92.7	77.95
Tag sequence Markov model, $\alpha$=0.95	92.65	88.47	88.12	79.65
Joint CRF model, $\alpha$=0.9	92.85	89.76	92.6	80.73

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Table 7. Analysis for the Influence of Filtering Rules on Morphological Tagging

Method(Joint CRF model, $\alpha$=0.9, $\beta$=0.1)	Accuracy (%)
	Stemming	Morpheme segmentation	POS	Overall
Joint CRF model, $\alpha$=0.9, $\beta$=0.1, When filtering rules are not used	92.85	89.76	92.6	80.73
Joint CRF model, $\alpha$=0.9, $\beta$=0.1, When filtering rules are used	97.4	94.58	96.35	92.58
Tag sequence transition model, $\alpha$=0.95, When filtering rules are used	94.35	93.22	94.78	91.81

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