Research the Method of Joint Segmentation and POS Tagging for Tibetan using BiGRU-CRF

Luo, Zhixiang; Zhu, Jie; Li, Zhensong; Liu, Saihu

doi:10.1145/3446132.3446395

Cited by 1 publication

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“…Conversely, the smaller the number of tags, the more efficient the model, but the less accurate it is. Considering the accuracy and efficiency of the model, this paper uses a quaternary tags set (B, I, E, and S) to represent the boundaries of a word [35]. B means the current character is the first character of the word, M means the current character is in the middle of the word, E means the current character is the last character of the word, and S means the current character is a single word.…”

Section: Labeling Strategymentioning

confidence: 99%

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging

Wang

2022

Computational Intelligence and Neuroscience

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Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rules. e simple matching or statistical analysis requires manual word segmentation followed by POS tagging, which leads to the inability to meet the practical requirements for label prediction accuracy. With the continuous development of deep learning technology, data-driven machine learning models provide new opportunities for automated Chinese word segmentation and POS tagging. erefore, a data-driven automated Chinese word segmentation and POS tagging model is proposed in order to address the above problems. Firstly, the main idea and overall framework of the proposed automated model are outlined, and the tagging strategy and neural network language model used are described. Secondly, two main optimisations are made on the input side of the model: (1) the use of word2Vec for the representation of text features, thus representing the text as a distributed word vector; and (2) the use of an improved AlexNet for e cient encoding of long-range word, and the addition of an attention mechanism to the model. Finally, on the output side, an additional auxiliary loss function was designed to optimise the Chinese text based on its frequency. e experimental results show that the proposed model can signi cantly improve the accuracy and operational e ciency of Chinese word segmentation and POS tagging compared with other existing models, thus verifying its e ectiveness and advancement.

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Section: Labeling Strategymentioning

confidence: 99%

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Research the Method of Joint Segmentation and POS Tagging for Tibetan using BiGRU-CRF

Cited by 1 publication

References 2 publications

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging

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