Back-Propagation Neural Network Approach to Myanmar Part-of-Speech Tagging

被引：2

作者：

Hnin, Hay Mar ^{[1
]}

Pa, Win Pa ^{[1
]}

Thu, Ye Kyaw ^{[2
]}

机构：

[1] Univ Comp Studies, Nat Language Proc Lab, Yangon, Myanmar

[2] Waseda Univ, Language & Speech Sci Res Lab, Tokyo, Japan

来源：

GENETIC AND EVOLUTIONARY COMPUTING | 2017年 / 536卷

关键词：

Part-of-Speech (POS) Tagging; Back-propagation Neural Network (BPNN); Hidden Markov Model (HMM); Myanmar language;

D O I：

10.1007/978-3-319-48490-7_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Part-of-Speech (POS) tagging is the process of assigning a POS label to each of a sequence of words. It is also a lowest level of syntactic analysis and useful for many natural language processing (NLP) tasks such as subsequent syntactic parsing and word sense disambiguation. We developed an annotated corpus and POS tagger for Myanmar language based on back-propagation neural network (BPNN) model. In our experiments, BPNN model is trained with 3gram, 4gram and 5gram. The results show that the BPNN model with 4 g is able to achieve considerable higher F-scores on the POS tagging task than 3 g and 5 g models for both close and open test sets. Moreover, BPNN POS tagging approach performed better than proposed HMM with rule based.

引用

页码：212 / 220

页数：9

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