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International Conference on Information and Automation (ICIA)

作者： Dequan Zheng Feng Yu Tiejun Zhao Sheng Li MOE-MS Key Laboratory ofNatural Language Processing and Speech Harbin Institute of Technology Harbin China School of Computer and Information Engineering Harbin University of Commerce Harbin China

For information retrieval, users hope to acquire more relevant information from the top N ranking documents. In this paper, a hybrid Chinese language model is presented, which is defined as a combination of ontology with statistical method, to improve the precision of top N ranking documents by reordering the initial retrieval documents. The experiment with NTCIR-3 formal Chinese test collection shows the proposed method improved the precision at top N ranking documents level

关键词： Natural languages Information retrieval Ontologies Indexing Statistical analysis Semantic Web Business laboratories speech processing Testing

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RESEARCH ON CHINESE INFORMATION RETRIEVAL BASED ON A HYBRID language MODELING

RESEARCH ON CHINESE INFORMATION RETRIEVAL BASED ON A HYBRID ...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： DE-QUAN ZHENG HAO YU TIE-JUN ZHAO SHENG LI FENG YU School of Computer and Information Engineering Harbin University of Commerce Harbin 150001 MOE-MS MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Har School of Computer and Information Engineering Harbin University of Commerce Harbin 150001

For information retrieval, users hope to acquire more relevant information from the top indexing documents. In this paper, a combination of Ontology with statistical method is presented to retrieval initial document set and improve the precision of top N ranking documents by re-ranking document set. The experiment with NTCIR-3 Chinese CLIR dataset shows the proposed method improved the precision of information retrieval.

关键词： Ontology statistical method linguistic Ontology knowledge information retrieval knowledge acquisition

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TEXT CLASSIFICATION BASED ON A COMBINATION OF ONTOLOGY WITH STATISTICAL METHOD

TEXT CLASSIFICATION BASED ON A COMBINATION OF ONTOLOGY WITH ...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： FENG YU DE-QUAN ZHENG SHENG LI TIE-JUN ZHAO HAO YU School of Computer and Information Engineering Harbin University of Commerce Harbin 150076 China School of Computer and Information Engineering Harbin University of Commerce Harbin 150076 China MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of TechnologyHarb

Text classification is becoming one of the key techniques in organizing and handling a large amount of text data. In this paper, a combination of ontology with statistical method is presented to improve the precision of text classification. In this study, first, different kind of linguistic ontology knowledge will be respectively acquired by learning training corpus to determine text classifiers. For an actual document,the semantic evaluation value of the document will respectively be gotten by different kind of linguistic ontology knowledge and the categories will be judged by the highest evaluation value. Compared with Bayes, k-nearest neighbor and support vector machine, the proposed approach outperforms previous works.

关键词： Text classification ontology statistical method linguistic ontology knowledge

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Two-stage approach to full Chinese parsing

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High technology Letters 2005年第4期11卷 359-363页

作者：曹海龙 Zhao Tiejun Yang Muyun Li Sheng MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 P.R. China

Natural language parsing is a task of great importance and extreme difficulty. In this paper, we present a full Chinese parsing system based on a two-stage approach. Rather than identifying all phrases by a uniform model, we utilize a divide and conquer strategy. We propose an effective and fast method based on Markov model to identify the base phrases. Then we make the first attempt to extend one of the best English parsing models i.e. the head-driven model to recognize Chinese complex phrases. Our two-stage approach is superior to the uniform approach in two aspects. First, it creates synergy between the Markov model and the head-driven model. Second, it reduces the complexity of full Chinese parsing and makes the parsing system space and time efficient. We evaluate our approach in PARSEVAL measures on the open test set, the parsing system performances at 87.53% precision, 87.95% recall.

关键词： natural language processing systems parsing markov model pattern recognition

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Chinese POS tagging based on bilexical co-occurrences

Chinese POS tagging based on bilexical co-occurrences

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Cao, Hai-Long Zhao, Tie-Jun Sheng, Li Sun, Jun Zhang, Chun-Xiang MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)078039092X

Chinese part of speech tagging is the basis of Chinese information processing. This paper proposes a method based on bilexical co-occurrences to tag Chinese text. The standard hidden Markov model assumes the transition between states (part of speech) is independent of the observation (word) sequence and the generation of a new observation is independent of other observations. In fact, Chinese text does not satisfy this assumption. Based on hidden Markov model, the effect of the words in the context on the decision of part of speech is also considered. The discriminative ability of the model is improved. Deleted interpolation is utilized to mitigate the data sparseness problem. We evaluate the proposed model on PFR China Daily corpus. The tagging accuracy is 99.09% on closed test and 96.37% on open test. ©2005 IEEE.

关键词： speech synthesis

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Research on an ontology-based language model

Research on an ontology-based language model

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International Conference on Chinese Computing 2005, ICCC 2005

作者： Zheng, Dequan Yu, Hao Zhao, Tiejun Li, Sheng Peng, Yuan MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Box 321 Harbin150001 China

ISBN: (纸本)9810530072

Ontology is widely applied to various areas of natural language processing, which regarded as a modeling tool of conceptual model of describing information in the level of semantic and knowledge. In this paper, we put forward an ontology-based language model with the ontological character, which is language-independent and can be used in cross-language information processing. Construct an ontology representation of feature information using a similarity framework;get the usage of feature information in using semantics, pragmatics, syntactic, etc. by specifically machine learning method and express as abstract ontology knowledge of feature information. processing actual document, the usage of feature information will be got in actual document and the similarity can be evaluated with ontology knowledge. We did three experiments in Chinese, i.e. IR, text similarity computing, specific information identification. The preliminary experiment testifies that recall and precision is advanced.

关键词： Ontology

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High precision English base noun phrase identification based on "waterfall" model

High precision English base noun phrase identification based...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Liang, Ying-Hong Zhao, Tie-Jun Yu, Hao Yao, Jian-Min MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 School of Information and Computer Engineering North East Forestry University Harbin 150040

ISBN: (纸本)078039092X

Based on classical model that used by software exploitation in the subject of Software Engineering - waterfall model, a high precision model for English noun phrase identification is presented. In this model, three important features (interior structure, context information and boundary character) in base noun phrase identification are orderly used, and the relative method (the rule method, the transfer- based and error-driven method, and the boundary statistic method) are used from top to bottom. Thus the precision of base noun phrase identification improved steadily. The "waterfall" model combined the rule method and the statistical method so as to make them complement each other. The precision of base noun phrase identification achieves 98.10% and the F score is 95.25%. Compared to other method, our method achieves the highest precision and F score. © 2005 IEEE.

关键词： Information analysis

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Distributed english text chunking using multi-agent based architecture

Distributed english text chunking using multi-agent based ar...

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4th Mexican International Conference on Artificial Intelligence, MICAI 2005

作者： Liang, Ying-Hong Zhao, Tie-Jun MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 School of Information and Computer Engineering North East Forestry University Harbin 150080

ISBN: (纸本)3540298967

The traditional English text chunking approach identifies phrases by using only one model and phrases with the same types of features. It has been shown that the limitations of using only one model are that: the use of the same types of features is not suitable for all phrases, and data sparseness may also result. In this paper, the Distributed Multi-Agent based architecture approach is proposed and applied in the identification of English phrases. This strategy put phrases into agents according to their sensitive features and identifies different phrases in parallel, where the main features are: one, easy and quick communication between phrases;two, avoidance of data sparseness. By applying and testing the approach on the public training and test corpus, the F score for arbitrary phrases identification using Distributed Multi-Agent strategy achieves 95.70% compared to the previous best F score of 94.17%. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Multi agent systems

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A multi-agent strategy for Chinese text chunking

A multi-agent strategy for Chinese text chunking

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Liang, Ying-Hong Zhao, Tie-Jun Mao, Lei MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 School of Information and Computer Engineering North East Forestry University Harbin 150040

ISBN: (纸本)078039092X

Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It is shown that one model couldn't comprise each phrase's characteristics, and same features are not suitable to all phrases, data sparseness also appears. Multi-agent strategy uses several model and sensitive features of each phrase to identify different phrases. This paper describes the Multi-agent strategy applied in the identification of Chinese phrases whose main features are: 1) easy and quick communication between phrases;2) avoidance of data sparseness. Through testing on Chinese Penn Treebank, F score of Chinese text chunking using Multi-agent strategy achieves to 95.82%, which is higher than the best result that has been reported. © 2005 IEEE.

关键词： Pattern recognition

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Machine learning for automatic acquisition of Chinese linguistic ontology knowledge

Machine learning for automatic acquisition of Chinese lingui...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zheng, De-Quan Zhao, Tie-Jun Yu, Feng Sheng-Li School of Computer Science and Engineering Harbin University of Commerce Harbin 150076 China MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)078039092X

Due to the complexity and flexibility of natural language, automatic linguistic knowledge acquisition and its application research becomes difficult. In this paper, we present a machine learning method to automatically acquire Chinese linguistic ontology knowledge from typical corpus. This study, first, defined the description frame of Chinese linguistic ontology knowledge, and then, automatically acquired the usage of a Chinese word with its co-occurrence of context in using semantic, pragmatics, syntactic, etc from the corpus, final, the above information and their representation will acted as Chinese linguistic ontology knowledge bank. We completed two groups of experiments, i.e. documents similarity computing, text reordering for information retrieval. Compared with previous works, the proposed method solves the inferior Precision of nature language processing. ©2005 IEEE.

关键词： Learning systems

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