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

Abstract

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.

Review of AlexNet for Medical Image Classification

“…Sharma, et al [52] demonstrated experimentally that the hierarchical finetuning method has potential for development. TL with AlexNet has enabled advances in various fields, including natural language processing [53,54], where researchers used the network's features to extract meaningful representations from text data.…”