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A part-of-speech tagging Model Employing Word Clustering and Syntactic Parsing

Chinese Journal of Electronics 2014年第1期23卷 109-114页

作者： YUAN Lichi School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

part-of-speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model,by employing word clustering and syntactic parsing ***, In order to overcome the defects of the classical HMM, Markov family model(MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The part-ofspeech tagging experiments show that the improved partOf-speech tagging model has higher performance than Hidden Markov models(HMMs) under the same testing conditions, the precision is enhanced from 94.642% to97.235%.

关键词： part-of-speech tagging Hidden Markov model Markov family model Word clustering

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Jointly part-of-speech tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

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Computers, Materials & Continua 2020年第10期65卷 529-541页

作者： Yatian Shen Yubo Mai Xiajiong Shen Wenke Ding Mengjiao Guo School of Computer Science and Engineering Southeast UniversityNanjing210000China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475000China Swinburne Data Science Research Institute Swinburne University of TechnologyVictoria3122Australia

Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic *** the same time,the predicate-argument structure in a sentence is important information for semantic role labeling *** this work,we introduce the auxiliary deep neural network model,which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role *** on the framework of joint learning,part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role *** addition,we introduce the argument recognition layer in the training process of the main task-semantic role labeling,so the argument-related structural information selected by the predicate through the attention mechanism is used to assist the main *** the model makes full use of the semantic dependency between part-of-speech and semantic roles and the structural information of predicate-argument,our model achieved the F1 value of 89.0%on the WSJ test set of CoNLL2005,which is superior to existing state-of-the-art model about 0.8%.

关键词： part-of-speech tagging semantic role labeling multi-task learning

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Improved hidden Markov model for speech recognition and POS tagging

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Journal of Central South University 2012年第2期19卷 511-516页

作者：袁里驰 School of Information Technology Jiangxi University of Finance and Economics School of Information Science and Engineering Central South University

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

关键词： hidden Markov model Markov family model speech recognition part-of-speech tagging

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A lexicalized second-order-HMM for ambiguity resolution in Chinese segmentation and POS tagging

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High Technology Letters 2005年第4期11卷 346-350页

作者：陈鄞 Yang Muyun Zhao Tiejun Yu Hao Li Sheng The Machine Translation Laboratory School of Computer Science and Engineering Harbin Institute of Technology Harbin 150001 P.R. China

Hidden Markov Model（HMM） is a main solution to ambiguities in Chinese segmentation anti POS （part-of-speech） tagging. While most previous works tot HMM-based Chinese segmentation anti POS tagging eonsult POS informatiou in contexts, they do not utilize lexieal information which is crucial for resoMng certain morphologieal ambiguity. This paper proposes a method which incorporates lexieal information and wider context information into HMM. Model induction anti related smoothing technique are presented in detail. Experiments indicate that this technique improves the segmentation and tagging accuracy by nearly 1%.

关键词： hidden Markov model chinese segmentation part-of-speech tagging

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A new method for automatic pattern acquisition to extract information from biomedical texts

A new method for automatic pattern acquisition to extract in...

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第七届国际信号处理会议

作者： Minlie Huang1 Xiaoyan Zhu1 Ming Li2 1Department of Computer Science and Technology Tsinghua University Beijing China 100084 2Bioinformatics Laboratory School of Computer Science University of Waterloo N2L 3G1 Canada

Recently there have been many information extraction tasks applied to the biomedical domain, some of which contribute to extract protein-protein interactions from biomedical texts. This paper prese

关键词： information extraction pattern acquisition part-of-speech tagging dynamic programming protein- protein interaction

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