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Study on the optimal parameters of image fusion based on wavelet transform

Journal of Computational Information Systems 2010年第1期6卷 131-137页

作者： Zheng, Hong Zheng, Dequan Hu, Yanxiang Li, Sheng MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China Tianjin Normal University Tianjin 300387 China

Selection of wavelet type, decomposition level and fusing rule is a key problem when wavelet transform is applied to image fusion. 2916 kinds of different fusing methods(54×5×9, including 54 wavelet types, 5 decomposing levels, and 9 fusing rules)are analyzed and compared in the experiment of fusing multi-focus images in this paper. Through calculating the comparability degree of fused images, the fusion performances are evaluated. And the experiment shows that the similarities of the results and the ideal pictures are all over 0.999, showing pretty good performance. Copyright © 2010 Binary Information Press.

关键词： Image fusion

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Event entailment chains extraction for Text-to-Scene conversion

Event entailment chains extraction for Text-to-Scene convers...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Han-Jing Li Zhen Li Xiao-Ping Xue Tie-Jun Zhao MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China Department of mathematics Harbin Institute of Technology Harbin China

In our study of Text-to-Scene conversation (TTS), which translates natural language into animations automatically, we realized that event entailment knowledge is useful in generating scenes since the main part of a scene is to show an event. In this paper, we provide some results of our attempt to extract event entailment knowledge. We use entailment chains instead of traditional entailment rules since the sequence of events is a process which make useful in TTS. The result shows that the work is worth to continue to study.

关键词： Animation Machine learning Cybernetics Joining processes Generators Natural language processing

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A tentative study on rule based metaphor identification

A tentative study on rule based metaphor identification

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yu Zhang Muyun Yang Xi Chen Sheng Li Tiejun Zhao MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China School of Foreign Languages Harbin Institute of Technology Harbin China

As one of the challenging issues in the field of Natural language processing (NLP), metaphor has aroused substantial attention among researchers in recent years. Many models and methods have been proposed for proper understanding of metaphors. But the automatic identification of metaphor is less touched. This paper presents a tentative study on the metaphor identification based on rules, and the results on a small scale corpus are provided.

关键词： Semantics Natural language processing Computational linguistics Tin Artificial intelligence Silicon Context

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Forced decoding for minimum error rate training in statistical machine translation

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Journal of Computational Information Systems 2012年第2期8卷 861-868页

作者： Liang, Huashen Zhang, Min Zhao, Tiejun MOE-MS Key Lab. of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China Institute for Infocomm Research FusionopolisWay #21-01 Connexis Singapore 138632 Singapore

We present a forced decoding approach for the tuning process in statistical machine translation. Unlike the traditional discriminative approaches, the forced decoding system can take advantage of the reference of development set to generate n-best list in the decoding stage, and then merge this list with original n-best list generated by decoder in the tuning stage. We find that traditional minimum error rate training process can not find good translations for long sentences. While the forced decoding system knows the references, it could generate translations that are more similar to the references, which means the results would have higher BLEU score. As a supplement, the result of forced decoding system would lead the tuning process to a better point. 1553-9105/Copyright © 2012 Binary Information Press.

关键词： Errors

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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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Syntax encapsulated phrase model for statistical machine translation

Syntax encapsulated phrase model for statistical machine tra...

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International Conference on Information Science and technology (ICIST)

作者： Huashen Liang Tiejun Zhao Yongzeng Xue MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China Department of New Media and Art Harbin Institute of Technology Harbin China

In the past few years, much attention has been paid on extending phrase-based statistical machine translation with syntactic structures. In this paper we introduce a novel syntax encapsulated phrase(SEP) model, in which treebank tag sequences are employed to decorate the bilingual phrase pairs. We use tag sequences, instead of phrase pairs, to train the lexicalized reordering model. Since the number of treebank tags is much smaller than the number of words, the tag sequence based reordering model is smaller and more accurate than the phrase based reordering model. Experiments were carried out on four types of models: the phrase model, the hierarchical phrase model, the POS tag encapsulated phrase(PTEP) model and the syntactic tag encapsulated phrase(STEP) model. The STEP model obtained higher BLEU-4 score than other models on NIST 2005 MT task.

关键词： Syntactics Training Vectors Computational modeling NIST Data models Mathematical model

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Inferring the Location of an Event in Large Corpus

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International Journal of Computer processing of languages 2011年第3期23卷 255-271页

作者： HANJING LI RUNZHI DONG TIEJUN ZHAO MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin Heilongjiang 150001 China Special Education School Beijing Union University Beijing 100075 China

The location of a passage is a kind of semantic information that may prove useful for a variety of applications dealing with inference over passages described in natural language texts. In this paper, we propose a method for automatic discovery of pairs of an event and a place term related by the location, such as wash clothes ⇒ laundry room. In contrast to previous approaches that extracting associations between particular actions and locations when those actions occur on dunning likelihood ratio, the underlying assumption of our method is that correlation between an event and a place term is in the regular co-occurrence of a verb and a place noun within locally coherent text. Based on the analogy with the problem of inferring semantic information from text corpus statistical method of Dunning's likelihood ratio is used to score the extracted pairs for association in order to discover the correlation in each pair. In our experimental evaluation, we examine the effect that various statistical methods produce on the accuracy of this model of inferring locations. After that we carried out a direct evaluation of rare pairs against different statistical method.

关键词： Semantic information inferring likelihood ratio rare event word association

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Maximum Entropy Model for Example-Based Machine Translation

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International Journal of Computer processing of languages 2007年第2N03期20卷 101-113页

作者： YIN CHEN MUYUN YANG SHENG LI MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology P.O. Box 321 No. 92 West Dazhi Street NanGang Harbin 150001 China

Most example-based machine translation (EBMT) systems handle their translation examples using some heuristic measures based on human intuition. However, these heuristic rules are usually hard to be effectively organized to scale to incorporate diverse features to cover more language phenomenon and large domains. In this paper, we use machine learning approach for EBMT model design instead of human intuition. Maximum entropy (ME) model is introduced in order to adequately incorporate different kinds of features inherited in the translation examples effectively. At the same time, a multi-dimensional feature space is formally constructed to include various features of different aspects. In the experiments, the proposed model shows significant performance improvement.

关键词： EBMT Machine learning Maximum entropy Feature space

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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 Multi-Agent Strategy Chinese Text for Both English and Chunking

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电子学报(英文版) 2006年第3期15卷 422-426页

作者： LIANG Yinghong ZHAO Tiejun YAO Jianmin YU Hao MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin 150001 China School of Information and Computer Engineering North East Forestry University Harbin 150080 China

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