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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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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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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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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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Research on Query Translation Disambiguation for CLIR Based on HowNet

Research on Query Translation Disambiguation for CLIR Based ...

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The 9th International Conference for Young Computer Scientists(第九届国际青年计算机大会)

作者： Honglei Zhu Dequan Zheng Tiejun Zhao MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001

Query translation is an important task for crosslanguage information retrieval (CLIR), which aims at translating the query described in source language into target language. The approach to query translation based on bilingual dictionary is becoming the mainstream thinking because of its simplicity and the increasing availability of machine readable bilingual dictionary. However, this kind of approach faces two necessary problems that is ambiguity in translation and the incompleteness of the dictionary. This paper focuses on the first problem, and it presents three statistical models based on HowNet to resolve query translation ambiguity of CLIR: Query translation selection based on semantic relation;Bilingual decaying co-occurrence model and 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 filters and optimizes the translation.

关键词： Query translation CLIR statistical method translation selection OOV.

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A new approach to query expansion in information retrieval

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High technology Letters 2008年第1期14卷 77-80页

作者：李卫疆 Zhao Tiejun Wang Xian＇gang MOE-MS Key Laboratory of Natural Language Processing and Speech -School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 P.R. China

To eliminate the mismatch between words of relevant documents and user's query and more seriousnegative effects it has on the performance of information retrieval,a method of query expansion on the ba-sis of new terms co-occurrence representation was put forward by analyzing the process of *** expansion terms were selected according to their correlation to the whole *** the sametime,the position information between terms were *** experimental result on test retrievalconference(TREC)data collection shows that the method proposed in the paper has made an improve-ment of 5%～19% all the time than the language modeling method without *** to thepopular approach of query expansion,pseudo feedback,the precision of the proposed method is competi-tive.

关键词： information retrieval language model query expansion

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Structure and Content Based Blog Pages Identification

Structure and Content Based Blog Pages Identification

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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

Blog is becoming more and more popular with the rapid development of Internet. It needs to find an automatic way to distinguish the blog pages from ordinary Web pages for the content extraction of blog pages and the blog community discovered. Some basic concepts and ideas in the area of blog was described in this paper, and a method on the blog pages identification is proposed, which is based on the blog pages structure and blog content. The experimentation shows that a high result can be achieved in precision.

关键词： Information services Web sites Internet Web pages Navigation Support vector machines Support vector machine classification Fuzzy systems Knowledge engineering Business

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A hybrid strategy to protein name recognition

A hybrid strategy to protein name recognition

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World Congress on Intelligent Control and Automation (WCICA)

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

This paper presents a comprehensive approach to identifying protein name in biomedical texts. The new method integrated the generalized Winnow algorithm and the heuristic rules to implement of initial detection of protein name. Moreover, the system introduced a statistic method to analyses the reliability of recognized protein boundary, which can be then used for expanding protein boundary which has low confidence. The experimental results show that this algorithm improves the whole performance for protein name recognition and that effective performance can be achieved in identifying boundary of protein name.

关键词： Protein engineering Proteins Algorithm design and analysis Electronic mail speech processing Automation Fault currents

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Multi-Agent Classifiers Fusion Strategy for Biomedical Named Entity Recognition

Multi-Agent Classifiers Fusion Strategy for Biomedical Named...

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International Conference on BioMedical Engineering and Informatics (BMEI)

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

Recognizing the biomedical named entity has become one of the most fundamental tasks in the biomedical knowledge discovery. The multi-agent classifiers fusion approach proposed here was found to efficiently recognize biomedical named entity. We employ conditional random fields as our underlying classifier model and incorporate diverse set of features into system, the relativity between classifiers is utilized by using co-decision matrix to exchange decision information among classifiers. The experiments are carried on GENIA corpus with the best result of 77.88% F-sore. The multi-agent classifier fusion strategy proposed here is obviously superior to the individual classifier based method and more effective than the classifiers fusion approach of boosting and bagging.

关键词： Hidden Markov models speech recognition Machine learning algorithms Machine learning Biomedical engineering Biomedical informatics Educational institutions Biomedical computing Information technology Petroleum

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Recent advances on NLP research in harbin institute of technology

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中国高等学校学术文摘·计算机科学 2007年第4期1卷 413-428页

作者： ZHAO Tiejun GUAN Yi LIU Ting WANG Qiang MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of TechnologyHarbin 150001China

In the 1960s,the researchers of harbin institute of technology(HIT)attempted to do relevant research on natural language *** more than 40-year's effort,HIT has already established three research laboratories for Chinese information processing,*** Machine Intelligence and Translation laboratory(MI&T lab),the Intelligent technology and Natural language processing laboratory(ITNLP)and the Information Retrieval laboratory (IR-lab).At present,it has a well-balanced research team of over 200 persons,and the research interests have extended to language processing,machine translation,text retrieval and other *** institute of technology has accumulated a batch of key techniques and data resources,won many prizes in the technical evaluations at home and *** institute of technology has become one of the most important natural language processing bases for teaching and scientific research in China *** paper gives an introduction to the achievements onNLP in HIT.

关键词： natural language processing harbin institute of technology text analysis machine translation

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