Search (25 results, page 1 of 2)

Chen, H.; Martinez, J.; Kirchhoff, A.; Ng, T.D.; Schatz, B.R.: Alleviating search uncertainty through concept associations : automatic indexing, co-occurence analysis, and parallel computing (1998) 0.02

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Theme: Semantisches Umfeld in Indexierung u. Retrieval

Chen, H.; Lally, A.M.; Zhu, B.; Chau, M.: HelpfulMed : Intelligent searching for medical information over the Internet (2003) 0.02

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Theme: Semantisches Umfeld in Indexierung u. Retrieval

Zheng, R.; Li, J.; Chen, H.; Huang, Z.: ¬A framework for authorship identification of online messages : writing-style features and classification techniques (2006) 0.01

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Date: 22. 7.2006 16:14:37

Dumais, S.; Chen, H.: Hierarchical classification of Web content (2000) 0.01

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Source: Proceedings of ACM SIGIR 23rd International Conference on Research and Development in Information Retrieval. Ed. by N.J. Belkin, P. Ingwersen u. M.K. Leong

Schatz, B.R.; Johnson, E.H.; Cochrane, P.A.; Chen, H.: Interactive term suggestion for users of digital libraries : using thesauri and co-occurrence lists for information retrieval (1996) 0.01

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Theme: Semantisches Umfeld in Indexierung u. Retrieval

Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012) 0.01
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Abstract

We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.

Ramsey, M.C.; Chen, H.; Zhu, B.; Schatz, B.R.: ¬A collection of visual thesauri for browsing large collections of geographic images (1999) 0.01

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Schroeder, J.; Xu, J.; Chen, H.; Chau, M.: Automated criminal link analysis based on domain knowledge (2007) 0.01

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Zhu, B.; Chen, H.: Validating a geographical image retrieval system (2000) 0.01

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Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998) 0.01

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Theme: Semantisches Umfeld in Indexierung u. Retrieval

Chen, H.; Yim, T.; Fye, D.: Automatic thesaurus generation for an electronic community system (1995) 0.01

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Editor: Schatz, B.

Marshall, B.; McDonald, D.; Chen, H.; Chung, W.: EBizPort: collecting and analyzing business intelligence information (2004) 0.01

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Chau, M.; Shiu, B.; Chan, M.; Chen, H.: Redips: backlink search and analysis on the Web for business intelligence analysis (2007) 0.01

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Marshall, B.; Chen, H.; Kaza, S.: Using importance flooding to identify interesting networks of criminal activity (2008) 0.01

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Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.01

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Chen, H.; Chung, W.; Qin, J.; Reid, E.; Sageman, M.; Weimann, G.: Uncovering the dark Web : a case study of Jihad on the Web (2008) 0.01

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Chen, H.; Ng, T.D.; Martinez, J.; Schatz, B.R.: ¬A concept space approach to addressing the vocabulary problem in scientific information retrieval : an experiment on the Worm Community System (1997) 0.00

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Qin, J.; Zhou, Y.; Chau, M.; Chen, H.: Multilingual Web retrieval : an experiment in English-Chinese business intelligence (2006) 0.00

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Chau, M.; Wong, C.H.; Zhou, Y.; Qin, J.; Chen, H.: Evaluating the use of search engine development tools in IT education (2010) 0.00

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Chen, H.: Intelligence and security informatics : Introduction to the special topic issue (2005) 0.00
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Abstract

Making the Nation Safer: The Role of Science and Technology in Countering Terrorism The commitment of the scientific, engineering, and health communities to helping the United States and the world respond to security challenges became evident after September 11, 2001. The U.S. National Research Council's report an "Making the Nation Safer: The Role of Science and Technology in Countering Terrorism," (National Research Council, 2002, p. 1) explains the context of such a new commitment: Terrorism is a serious threat to the Security of the United States and indeed the world. The vulnerability of societies to terrorist attacks results in part from the proliferation of chemical, biological, and nuclear weapons of mass destruction, but it also is a consequence of the highly efficient and interconnected systems that we rely an for key services such as transportation, information, energy, and health care. The efficient functioning of these systems reflects great technological achievements of the past century, but interconnectedness within and across systems also means that infrastructures are vulnerable to local disruptions, which could lead to widespread or catastrophic failures. As terrorists seek to exploit these vulnerabilities, it is fitting that we harness the nation's exceptional scientific and technological capabilities to Counter terrorist threats. A committee of 24 of the leading scientific, engineering, medical, and policy experts in the United States conducted the study described in the report. Eight panels were separately appointed and asked to provide input to the committee. The panels included: (a) biological sciences, (b) chemical issues, (c) nuclear and radiological issues, (d) information technology, (e) transportation, (f) energy facilities, Cities, and fixed infrastructure, (g) behavioral, social, and institutional issues, and (h) systems analysis and systems engineering. The focus of the committee's work was to make the nation safer from emerging terrorist threats that sought to inflict catastrophic damage an the nation's people, its infrastructure, or its economy. The committee considered nine areas, each of which is discussed in a separate chapter in the report: nuclear and radiological materials, human and agricultural health systems, toxic chemicals and explosive materials, information technology, energy systems, transportation systems, Cities and fixed infrastructure, the response of people to terrorism, and complex and interdependent systems. The chapter an information technology (IT) is particularly relevant to this special issue. The report recommends that "a strategic long-term research and development agenda should be established to address three primary counterterrorismrelated areas in IT: information and network security, the IT needs of emergency responders, and information fusion and management" (National Research Council, 2002, pp. 11 -12). The MD in information and network security should include approaches and architectures for prevention, identification, and containment of cyber-intrusions and recovery from them. The R&D to address IT needs of emergency responders should include ensuring interoperability, maintaining and expanding communications capability during an emergency, communicating with the public during an emergency, and providing support for decision makers. The R&D in information fusion and management for the intelligence, law enforcement, and emergency response communities should include data mining, data integration, language technologies, and processing of image and audio data. Much of the research reported in this special issue is related to information fusion and management for homeland security.

Search (25 results, page 1 of 2)

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