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Asia-Pacific Conference on Information processing, APCIP

作者： Mei-ying Jia Bing-ru Yang De-quan Zheng Wei-cong Sun Li Liu Jing Yang School of Information Engineering University of Science and Technology Beijing Beijing China MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China

Ontology is a method of expressing knowledge. Now the research on the automatic ontology construction has become very popular. In this paper, we introduce the principles of ontology construction, as well as the several methods of automatic construction and particularly analyze their characteristics. Finally, the ontology in military intelligence is constructed automatically by using the existing resources, thesaurus and database. The thesaurus provides classes information for the ontology, while the database provides the instances. The test result shows the accuracy of the semantic relations in the ontology is 86.23%, and the ontology includes the complete information on the instances.

关键词： Ontologies Natural language processing Knowledge engineering Laboratories speech processing Thesauri Information processing Sun Deductive databases Testing

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Automatic Domain Ontology Construction Based on Thesauri

Automatic Domain Ontology Construction Based on Thesauri

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Weicong Sun Meiying Jia Dequan Zheng Hongqiang Cao Bingru Yang Hao Yu MOE MS Key Laboratory of Natural Language processing and Speech Harbin Institute of Technology Harbin China School of Information Engineering University of Science and Technology Beijing China Beijing Graphics Institute Beijing China

The research on the automatic ontology construction has become very popular. It is very useful for the ontology construction to reengineer the existing knowledge resource, such as the thesauri. But many relationships in the thesauri are incorrect or are defined too broadly. Accordingly, extracting ontological relations from the thesauri becomes very important. This paper proposes the method to reengineer the thesauri to ontology, and especially the method to how to obtain the correct semantic relations. The test result shows the accuracy of the semantic relations is86.23%, and one is the hierarchical relations with 89.02%, and the other is non-hierarchical relations with 83.44%.

关键词： Ontologies Thesauri Terminology Knowledge engineering Logic Fuzzy systems Laboratories Natural language processing speech processing Graphics

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Query Expansion with Statistical Machine Translation

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电子学报(英文版) 2008年第1期17卷 48-52页

作者： LI Weijiang ZHAO Tiejun WANG Xiangang MOE-MS Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

In practical applications of information retrieval, such as the search engine, the query user submitted contains only several keywords usually. This will cause unmatched issues of words between relevant files and the user's query, and result in more seriously negative effects on the performance of information retrieval. On the basis of analyzing the process of producing query, this paper puts forward a new method of query expansion based on the model of statistical machine translation. The approach extract related terms between documents and query through statistical machine translation model, then expand the query with them. The experiment on TREC data collection shows that our method achieved 4-17% of the improvement all the time more than the language model method without expanding. Compared to pseudo feedback, our method has the competitive average precision.

关键词：信息检索机器翻译查询扩展语言模型

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RM-structure alignment based statistical machine translation model

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High Technology Letters 2008年第3期14卷 271-275页

作者：孙加东 Zhao Tiejun MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 P.R. China

A novel model based on structure alignments is proposed for statistical machine translation in this paper. Meta-structure and sequence of meta-structure for a parse tree are defined. During the translation process, a parse tree is decomposed to deal with the structure divergence and the alignments can be constructed at different levels of recombination of meta-structure （RM）. This method can perform the structure mapping across the sub-tree structure between languages. As a result, we get not only the translation for the target language, but sequence of meta-stmctu .re of its parse tree at the same time. Experiments show that the model in the framework of log-linear model has better generative ability and significantly outperforms Pharaoh, a phrase-based system.

关键词： statistical machine translation recombination of meta-structure （ RM） structure alignment log-linear model

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AN EFFICIENT APPROACH TO IMPORTANT BLOGGERS DISCOVERY

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Journal of Electronics(China) 2008年第2期25卷 218-225页

作者： Yang Yuhang Yu Hao Zhao Tiejun Tan Hongye Zheng Dequan MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China

Popularity of blogs and the amount of information in the blogosphere increase so fast that it is difficult for Internet users to search the information they care about. Compared with conventional webs,links in the blogosphere are more abundant and conversations between bloggers are more fre-quent. This paper proposes a method of ranking bloggers based on link analysis,which can exemplify the characteristics of blogs,and reduce the influence of link spamming. This method can also bring convenience to users to read blogs,and it can supply a new methodology for information retrieval in the blogosphere. To ensure the reliability of the ranking results,some evaluation indicators of the im-portant bloggers are proposed,and the grading results of bloggers using the proposed method is compared with that using other indicators. At last,correlation analysis is used to verify the consistency between the proposed method and the evaluation indicators.

关键词： Important blogger Link analysis Evaluation indicator Correlation analysis

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Classifiers ensemble approaches for automatic recognition of biomedical named entities

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Journal of Computational Information Systems 2008年第3期4卷 1001-1008页

作者： Wang, Haochang Zhao, Tiejun College of Computer and Information Technology Daqing Petroleum Institute Daqing 163318 China MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China

As a new branch of data mining and knowledge discovery, the research of biomedical text mining has a rapid progress currently. Biomedical named entity recognition is a basic technique in the biomedical knowledge discovery and its performance has direct effects on further discovery and processing in biomedical texts. In this paper, we present classifiers ensemble approaches for biomedical named entity recognition. Four individual classifiers, Generalized Winnow, Conditional Random Fields, Support Vector Machine, and Maximum Entropy are combined through three different strategies. We demonstrate the effectiveness of the strategies and compare their performances with standalone classifier system. The experiments are carried on JNLPBA2004 corpus with an F-sore of 77.57%. Experimental results show that the proposed method, stacking ensemble strategy, can yield promising performances.

关键词： Data mining

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Japanese NER Post-processing Based on Improved TBL Method

Japanese NER Post-Processing Based on Improved TBL Method

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Dequan Zheng Jing Wang Tiejun Zhao MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology

A TBL based post-processing approach is proposed for Japanese named entity recognition (NER) in this paper. Firstly, tuning rules are automatically acquired from the results of Japanese NER by error-driven learning. And then, the tuning rules are optimized according to given threshold conditions. After filtered, the rules are used to revise the results of Japanese NER. Above all, this approach could be used in special domains perfectly for its learning domain linguistic knowledge automatically. The learnt rules could not go over fit as well. The experimental results show that a high result can be achieved in precision for Japanese NER.

关键词： Hidden Markov models Degradation Error correction Fuzzy systems Laboratories Natural language processing speech processing Graphics speech recognition Automatic speech recognition

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A Fusion of Multiple Classifiers Approach Based on Reliability function for Text Categorization

A Fusion of Multiple Classifiers Approach Based on Reliabili...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Qingxuan Chen Dequan Zheng Tiejun Zhao Sheng Li MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China

With the development of Internet and the rapid expansion of electronic resource, text classification technology is becoming an effective organization and management tool to deal with information. In this paper, a method for text categorization based on the fusion of multiple classifiers was presented, reliability function was introduction to select the text that hard to give determine by the main classifier, for these texts, multiple classifiers were used to give the determine which category the unlabeled documents belong to by voting. Experiments showed that the performance of text classification improved by the proposed method. Compared with single classifier, this method achieved better performance, only increasing a small amount of time than using single main classifier. Besides this, this method is more stable than using single classifier for text categorization task, especially when using different corpuses to check the performance of various methods.

关键词： Text categorization Voting Support vector machines Support vector machine classification Frequency shift keying Fuzzy systems Laboratories Natural language processing speech processing Internet

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Identifying named entities in biomedical text based on stacked generalization

Identifying named entities in biomedical text based on stack...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Haochang Wang Tiejun Zhao MS Key Laboratory of Natural Language Processing and Speech in Harbin Institute of Technology MOE Harbin China

Biomedical named entity recognition is a basic technique in the biomedical knowledge discovery and its performance has direct effects on further discovery and processing in biomedical texts. In this paper, we present stacked generalization strategy for biomedical named entity recognition including homogeneous classifier ensembles and heterogeneous classifier ensembles based on stacked generalization. Evaluations show that stacked generalization strategy can take advantage of more useful evidences, and make use of compensation and relativity among different classifiers to learn the correlation between individual classifiers predictions and the correct prediction to improve the performances of the system. This method breaks through the limitation of single classifier and achieves promising performances.

关键词： Classification algorithms Training Data mining Stacking Data models Support vector machine classification Training data

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Query Translation Selection for Cross-language Information Retrieval Based on HowNet

Query Translation Selection for Cross-Language Information R...

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The 2008 IEEE International Conference on Natural language processing and Knowledge Engineering(IEEE NLP-KE 2008)(2008IEEE自然语言处理与知识工程国际会议)

作者： Honglei ZHU Dequan ZHENG Tiejun ZHAO MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of TechnologyHarbin 150001

Research on cross-language information retrieval (CLIR) increasingly concentrates in candidate translation selection of the keywords in the query. The accuracy of translation has a direct impact on accurate rate and recalled rate. This thesis presents three methods based on HowNet to resolve query translation ambiguity of CLIR. The first is based on semantic relation, and it uses semantic relation network of context to determine the semantic of keywords and then select the correct translation. Bilingual decaying co-occurrence model count bilingual corpus co-occurrence information which includes the times and distance value of co-occurrence, which is different from monolingual co-occurrence. To resolve the problem of sparseness in corpus and make full use of the bilingual corpus, this paper gives another model that is semantic decaying co-occurrence model. Through test and summarizing this paper gets the best algorithm to integrate the traits of the three models, which gradually optimizes the translation and gets a higher precision.

关键词： Query translation CLIR statistical method translation selection OOV

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