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Building bilingual ontology from WordNet and Chinese classified thesaurus

2nd International Conference on knowledge Science, engineering and Management, KSEM 2007

作者： He, Hu Xiaoyong, Du School of Information Renmin University of China 100872 Beijing China Key Lab. of Data and Knowledge Engineering MOE 100872 Beijing China

ISBN: (纸本)9783540767183

Both WordNet and Chinese Classified Thesaurus(CCT) are widely used in information retrieval and management systems. In this paper we propose a novel approach for building bilingual ontologies based on these existing knowledge bases, WordNet and CCT. The bilingual ontology has the merit that contains both domain related and general purpose semantic information coverage from these two complementary knowledge sources. A lattice based similarity measure and assessment algorithm is used for aligning and merging these two knowledge bases. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Ontology

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A query-oriented XML text summarization for mobile devices

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SOFT COMPUTING 2013年第9期17卷 1585-1593页

作者： Liu, Dexi Wu, Shihan Lan, Yuehua Di, Guoqiang Peng, Jiezhao Xiong, Naixue Vasilakos, Athanasios V. Jiangxi Univ Finance & Econ Nanchang 330013 Peoples R China Jiangxi Key Lab Data & Knowledge Engn Nanchang 330013 Peoples R China Shanghai Rural Commercial Bank Songjiang Branch Shanghai 201600 Peoples R China Gannan Med Univ Ganzhou 341000 Peoples R China Univ Western Macedonia Dept Comp Engn Kozani 56100 Greece

Extensible Markup Language (XML) is a simple, flexible text format derived from SGML, which is originally designed to support large-scale electronic publishing. Nowadays XML plays a fundamental role in the exchange of a wide variety of data on the Web. As XML allows designers to create their own customized tags, enables the definition, transmission, validation, and interpretation of data between applications, devices and organizations, lots of works in soft computing employ XML to take control and responsibility for the information, such as fuzzy markup language, and accordingly there are lots of XML-based data or documents. However, most of mobile and interactive ubiquitous multimedia devices have restricted hardware such as CPU, memory, and display screen. So, it is essential to compress an XML document/element collection to a brief summary before it is delivered to the user according to his/her information need. Query-oriented XML text summarization aims to provide users a brief and readable substitution of the original retrieved documents/elements according to the user's query, which can relieve users' reading burden effectively. We propose a query-oriented XML summarization system QXMLSum, which extracts sentences and combines them as a summary based on three kinds of features: user's queries, the content of XML documents/elements, and the structure of XML documents/elements. Experiments on the IEEE-CS datasets used in Initiative for the Evaluation of XML Retrieval show that the query-oriented XML summary generated by QXMLSum is competitive.

关键词： Mobile devices Query-oriented XML text summarization Query expansion Content and structure

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Two types of generalized variable precision formal concepts

Two types of generalized variable precision formal concepts

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International Conference on Rough Sets and Intelligent Systems Paradigms

作者： Yang, Hong-Zhi Shao, Ming-Wen Xi An Jiao Tong Univ Fac Sci Xian 710049 Shaanxi Peoples R China Pingyuan Univ Xinxiang 453003 Henan Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Jiangxi Key Lab Data & Knowledge Engn Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9783540734505

In this paper, we introduce two pairs operators in fuzzy formal contexts. Based on the proposed operators, we present two types of generalized variable precision formal concepts, i.e. property oriented crisp-fuzzy concepts and object oriented fuzzy-crisp concepts. We have different level generalized formal concepts with different precision level. Last, we discuss the relationship between different precision level generalized concepts lattices in details.

关键词： formal concept analysis fuzzy formal context concept lattice variable precision

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An Effective Framework for Identifying Good XML Feedback Fragments

An Effective Framework for Identifying Good XML Feedback Fra...

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12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE) / 9th International Conference on Computer Science and its Applications (CSA)

作者： Zhong, Minjuan Zou, Beiji Wang, Lei Liao, Shumei Xiong, Naixue South Cent Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Jiangxi Key Lab Data & Knowledge Engn Nanchang Jiangxi Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA

ISBN: (纸本)9789811076053;9789811076046

In Pseudo relevance feedback, It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. For Extensible Markup Language (XML) data, this paper proposes an approach for identifying good feedback fragments by a complete framework in which two phrases are included. (1) The first phase is about XML search results clustering. We performed a k-medoid clustering algorithm to XML fragments based on an extended latent semantic indexing model. (2) The second phase is a two-stage ranking. Cluster ranking is performed in the first stage to select relevant clusters on the basis of cluster labelling, which is determined by extracted fragment keywords based on a combination of weight and context;fragment ranking is performed during the second stage where multiple features are used to identify high quality fragments from the previously obtained relevant clusters. Experimental results on standard INEX test data show that the proposed approach achieves statistically significant improvements over a strong original query results mechanism, ensuring high quality fragments for feedback.

关键词： XML fragment Clustering search results Two-stage ranking model Pseudo relevance feedback

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Chinese morpheme-based emotion detection on financial securities information

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Journal of Convergence Information Technology 2012年第20期7卷 406-418页

作者： Hairong, Bian Changxuan, Wan Guolin, Li Li, Yang School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Lab of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China

With the unceasing improvement of data mining and natural language processing technology, more and more researchers devote themselves to comment resources on the Web. While there are no readymade emotional corpora in the financial and securities domain, emotional analysis applied in this field is still rare. As financial information is usually in the form of unstructured Web Text, this paper fully considers characteristics of financial information, and analyzes the emotion of Web Text by calculating their emotional inclination values based on evaluation scores of morpheme. For each document we compute an emotional value. Its symbol indicates the emotional inclination, and its absolute value reflects the emotional intensity. Thus, this can prevent the limitation of lacking emotional corpus in the financial and securities domain. Experimental results demonstrate that this presented scheme optimizes the existing main schemes to effectively analyze the emotional inclinations of Web financial information.

关键词： Sentiment analysis

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination 20

On-Line System of Garbage Image-Orientated Intelligent Class...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Tian, Jiayin Wang, Yaozhi Liu, Jiaxin Chen, Yan School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350365856

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described. © 2024 IEEE.

关键词： Zero-shot learning

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A collaborative join scheme on a MIC-based heterogeneous platform

A collaborative join scheme on a MIC-based heterogeneous pla...

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Lecture Notes in Computer Science

作者： Zhou, Kailai Sun, Hui Chen, Hong Wu, Tianzhen Li, Cuiping Key Lab of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China School of Computer and Information Southwest Forestry University Kunming China

ISBN: (纸本)9783319458168

Join is one of the most important operations in data analytics systems. Prior works focus mainly on join optimization using GPUs, but little is known about performance impact on the MICs. In order to investigate potential benefits of the use of MIC accelerators in improving performance of join operation, in this paper we design a join scheme with a CPU and MICs working collaboratively. This scheme includes task partitioning, a data transfer mode, join algorithm design. Experimental results show that our collective join scheme is effective for a heterogeneous platform with two Xeon Phi cards, and can improve performance by up to 30 % over the CPU-only platform. © Springer International Publishing Switzerland 2016.

关键词： data transfer

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Constructing Corpus for Query-oriented XML Text Summarization

Constructing Corpus for Query-oriented XML Text Summarizatio...

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2010 International Conference on Management of e-Commerce and e-Government(第四届电子商务与电子政务管理国际会议 ICMeCG 2010)

作者： Shihan WU Dexi LIU Xianpei JIAO Jiangxi Key Laboratory of Data and Knowledge Engineering School of Information Technology Jiangxi University of Finance & Economics Nanchang Jiangxi China

XML Retrieval is becoming the focus study of the field of Information Retrieval and database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, as the basis of this study, the construction of the query-oriented XML text summarization corpus has not yet received enough attention. In this paper, we introduce our works on constructing this kind of corpus, including the selection of topics and XML elements/documents, construction process and the feature of the constructed corpus. Up to now, the corpus has 25 English query topics, including 422 elements for summarization, and 32 Chinese topics which including 402 elements. For each topic, 4 pieces of extracted summaries and 4 pieces of generated summaries are made manually by 4 experts.

关键词： Query-oriented XML Automatic summarization Corpus

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Speech emotion recognition with acoustic and lexical features 40

Speech emotion recognition with acoustic and lexical feature...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Jin, Qin Li, Chengxin Chen, Shizhe Wu, Huimin Computer Science Department School of Information Renmin University of China Beijing China Key Lab of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing China

ISBN: (纸本)9781467369978

In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%. © 2015 IEEE.

关键词： Acoustic features Emotion lexicon Emotion recognition Lexical features Support vector machine

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