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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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A Novel Linguistic Phenomenon Description for Text Similarity Computing

A Novel Linguistic Phenomenon Description for Text Similarit...

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China-Ireland International Conference on Information and Communications Technologies, CIICT 2007

作者： Zheng, Dequan Zhao, Tiejun Li, Sheng Yang, Muyun MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China

ISBN: (纸本)9780863418273

A solution of computing text similarity was presented in this paper, which was based on a novel linguistic phenomenon description. In this study, word sense ontology of keyword is firstly constructed by context multi-information, and then, the same feature firstly was acquired from text pairs, the usage of context co-occurrence feature was gotten in using part of speech, semantic, location, average co-occurrence probability, and was expressed as the linguistic ontology knowledge;final, text similarity evaluation value is calculated for each text to judge the text similarity degree. The Chinese document set from the NTCIR-3 workshop collection was used to evaluate the method, it shows that an average 15.45%-18.49% and 11.96%-15.35% increase in precision can be achieved at top 10 and 100 ranking documents level respectively.

关键词： Ontology

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The extraction of trajectories from real texts based on linear classification 16

The extraction of trajectories from real texts based on line...

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16th Nordic Conference of Computational Linguistics, NODALIDA 2007

作者： Li, Hanjing Zhao, Tiejun Li, Sheng Zhao, Jiyuan Harbin Institute of Technology MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin150001 China

ISBN: (纸本)9789985405130

Text-to-scene conversion systems need to share the spatial descriptions between natural language and the 3D scene. Such applications are the ideal candidates for the extraction of spatial relations from free texts, in which the extraction to trajectories that are focus objects in spatial descriptions is an essential problem. We present an analysis of how the space relations are described in Chinese. Based on this study, we propose a method where the extraction of trajectories is modeled as a binary classification problem and resolved based on a linear classifier with syntactic features. Moreover, experimental results are analyzed in detail to demonstrate the effectiveness of the linear classifier to the extraction problem of the trajectory concept. © 2007 Proceedings of the 16th Nordic Conference of Computational Linguistics, NODALIDA 2007. All rights reserved.

关键词： Extraction

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PARSING CHINESE TEXT BASED ON SEMANTIC CLASS

PARSING CHINESE TEXT BASED ON SEMANTIC CLASS

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： HUA-FU DING SHENG LI TIE-JUN ZHAO MOE-MS Key Laboratory of Natural Language Processing and Speech Box 321 Harbin Institute of Technol MOE-MS Key Laboratory of Natural Language Processing and Speech Box 321 Harbin Institute of Technol

This paper proposes a novel Chinese syntactic parsing model based on semantic class, which is a variant of normal lexicalized statistical *** attempts to make use of the syntactic and semantic similarity between Chinese words and then produces a more knowledgeable estimate of the probability of grammar rules.A simple but effective unsupervised method is designed to determine the proper semantic class of given *** class is used to improve the performance of parsing *** evaluate our methods on the widely used Penn Chinese *** results show that it outperforms a famous lexicalized model significantly on appropriate semantic class levels.

关键词： Chinese information processing Parsing Semantic class

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A Divide-Conquer Strategy for Both English and Chinese Text Chunking

A Divide-Conquer Strategy for Both English and Chinese Text ...

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International Conference on Advanced language processing and Web Information Technology (ALPIT)

作者： Ying-hong Liang Ni-Hong Wang Zhao-wen Qiu Yin-Chen Tie-jun Zhao MOE-MS Key Laboratory of Natural Language Processing and Speech in Harbin Institute of Technology Harbin China School of Information and Computer Engineering in North East Forestry University Harbin China MOE-MS Key Laboratory of Natural Language Processing and Speech in Harbin Institute of Technology Harbin

ISBN: (纸本)9780769529301;0769529305

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, a divide-conquer strategy is proposed and applied in the identification of English phrases. And then, this strategy is rapid transplanted to Chinese text chunking. This strategy divides the task of chunking into several sub-tasks according to sensitive features of each phrase and identifies different phrases in parallel. Then, a two-stage decreasing conflict strategy is used to synthesize each sub-task's answer, where the main features are: one, each phrase uses its own sensitive features; two, avoidance of data sparseness. Through testing on public corpus (English) and Chinese Penn Treebank (Chinese), F score of English chunking achieves to 95.14% and that of Chinese chunking is 95.23%. These results are state of the art with the best results that have been reported..

关键词： Natural language processing Natural languages Information technology Forestry Laboratories speech processing Electronic mail Testing Learning systems Data mining

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