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A New part-of-speech Tagging System Based on Closed-words,Word Form and Rules

Journal of Harbin Institute of Technology(New Series) 1999年第1期6卷 43-45页

作者：吴岩李修昆王开铸

A method of part-of-speech tagging of English text based on closed-words, wold-form and rules, its abstract model and formal description of its realizing procedure are presented. Finally, an experimental example is gi... 详细信息

关键词： part-of-speech tagging closed-words

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Research on calculation method of text similarity based on smooth inverse frequency

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The Journal of China Universities of Posts and Telecommunications 2020年第2期27卷 56-64页

作者： Yuan Ye Yu Minmin Liu Jiming Key Laboratory of E-commerce and Modem Logistics Chongqing University of Posts and TelecommunicationsChongqing 400065China

In order to improve the accuracy of text similarity calculation,this paper presents a text similarity function part of speech and word order-smooth inverse frequency(PO-SIF)based on sentence vector,which optimizes the classical SIF calculation method in two aspects:part of speech and word order.The classical SIF algorithm is to calculate sentence similarity by getting a sentence vector through weighting and reducing noise.However,the different methods of weighting or reducing noise would affect the efficiency and the accuracy of similarity calculation.In our proposed PO-SIF,the weight parameters of the SIF sentence vector are first updated by the part of speech subtraction factor,to determine the most crucial words.Furthermore,PO-SIF calculates the sentence vector similarity taking into the account of word order,which overcomes the drawback of similarity analysis that is mostly based on the word frequency.The experimental results validate the performance of our proposed PO-SIF on improving the accuracy of text similarity calculation.

关键词： word2vec SIF part-of-speech word order similarity

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A Study on Comparison of Words in Ancient and Modern Chinese and Foreign Fantasy Novels

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海外英语 2018年第9期 166-168页

作者：朱旭罡宋肇鹏杨昊堃 School of Foreign Languages of Huazhong University of Science and Technology Wnhan 430074China

Fantasy novel is a kind of novel literature, which is different from other fictions. With the passage of time, fantasy novels have great development with human society. There are differences and similarities between Chinese and Western fantasy novels,which make literary works widely spread and popular. This study uses corpus linguistics software(Tree-Tagger, Range) to analyze four famous Chinese and English fantasy novels and their English translation, observing the different of proportion of noun, verb,adverb and adjective and the lexical difficulty of the novel.

关键词： Fantasy novel part-of-speech Range I106 A

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TPOS Tagging Method Based on BiLSTM_CRF Model

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国际计算机前沿大会会议论文集 2019年第1期 501-503页

作者： Lili Wang Ziyan Chen Hongwu Yang College of Physics and Electronic Engineering Northwest Normal University Lanzhou 730070 China Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Lanzhou 730070 China National and Provincial Joint Engineering Laboratory of Learning Analysis Technology in Online Education Lanzhou 730070 China

part of speech (POS) tagging determines the attributes of each word, and it is the fundamental work in machine translation, speech recognition, information retrieval and other fields. For Tibetan part-of-speech (TPOS) tagging, a tagging method is proposed based on bidirectional long short-term memory with conditional random field model (BiLSTM_CRF). Firstly, the designed TOPS tagging set and manual tagging corpus were used to get word vectors by embedding Tibetan words and corresponding TPOS tags in continuous bag-of-words (CBOW) model. Secondly, the word vectors were input into the BiLSTM_CRF model. To obtain the predictive score matrix, this model using the past input features and future input feature information respectively learned by forward long short-term memory (LSTM) and backward LSTM performs non-linear operations on the softmax layer. The prediction score matrix was input into the CRF model to judge the threshold value and calculate the sequence score error. Lastly, a Tibetan part of speech tagging model was got based on the BiLSTM_CRF model. The experimental results indicate that the accuracy of TPOS tagging model based on the BiLSTM_CRF model can reach 92.7%.

关键词： Deep learning Word embedding model Tibetan part-of-speech

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The Contribution of Mutual Information in the Intonational Phrase Prediction In Chinese Text

The Contribution of Mutual Information in the Intonational P...

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2003 International Conference on Natural Language Processing and Knowledge Engineering

作者： Guo-Ping HU Ben-Feng CHEN Ming FAN Ren-Hua WANG University of Science and Technology of China

ISBN: (纸本)0780379020

The contribution of Mutual Information(MI) in the Intonational Phrase(IP) prediction is analyzed and verified in this paper.The basic idea of employing MI in IP prediction is that people is likely to pause between the less correlated words,where the MI value is low.The paper presents a decision tree based predictor which adopts POS as the main feature firstly as the baseline,and then the paper analyzes the correlation between MI and IP.The approach which only bases on the MI to predict the IP boundary is demonstrated in this paper,and three methods combining the MI and POS in the predictor is presented too.In the MI based approach,a considerable performance(F-Score:64.2%) is achieved,and 3.4% promotion from the baseline is achieved after combining MI and POS in our experiment.All our work indicates that MI is an effective feature in the prosodic phrase boundaries prediction in Chinese text, and combining the MI and POS in the predictor is valuable.

关键词： Intonational Phrase Mutual Information Prosody Boundary part-of-speech

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