-
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)
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- Date
- 10. 8.2001 21:23:46
-
Chung, W.; Chen, H.; Reid, E.: Business stakeholder analyzer : an experiment of classifying stakeholders on the Web (2009)
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- Date
- 23. 2.2009 18:36:23
-
Chau, M.; Shiu, B.; Chan, M.; Chen, H.: Redips: backlink search and analysis on the Web for business intelligence analysis (2007)
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- Date
- 7. 3.2007 16:19:23
-
Chen, H.: Generating, integrating and activating thesauri for concept-based document retrieval (1993)
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- Theme
- Konzeption und Anwendung des Prinzips Thesaurus
-
Chen, H.; Ng, T.: ¬An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation) : symbolic branch-and-bound search versus connectionist Hopfield Net Activation (1995)
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- Source
- Journal of the American Society for Information Science. 46(1995) no.5, S.348-369
- Theme
- Konzeption und Anwendung des Prinzips Thesaurus
-
Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012)
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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.
- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.889-903
-
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)
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- Theme
- Konzeption und Anwendung des Prinzips Thesaurus
-
Chen, H.: ¬An analysis of image queries in the field of art history (2001)
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- Abstract
- Chen arranged with an Art History instructor to require 20 medieval art images in papers received from 29 students. Participants completed a self administered presearch and postsearch questionnaire, and were interviewed after questionnaire analysis, in order to collect both the keywords and phrases they planned to use, and those actually used. Three MLIS student reviewers then mapped the queries to Enser and McGregor's four categories, Jorgensen's 12 classes, and Fidel's 12 feature data and object poles providing a degree of match on a seven point scale (one not at all to 7 exact). The reviewers give highest scores to Enser and McGregor;'s categories. Modifications to both the Enser and McGregor and Jorgensen schemes are suggested
-
Chen, H.; Yim, T.; Fye, D.: Automatic thesaurus generation for an electronic community system (1995)
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- Theme
- Konzeption und Anwendung des Prinzips Thesaurus
-
Carmel, E.; Crawford, S.; Chen, H.: Browsing in hypertext : a cognitive study (1992)
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- Source
- IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884
-
Leroy, G.; Chen, H.: Genescene: an ontology-enhanced integration of linguistic and co-occurrence based relations in biomedical texts (2005)
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- Date
- 22. 7.2006 14:26:01
- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.5, S.457-468
-
Suakkaphong, N.; Zhang, Z.; Chen, H.: Disease named entity recognition using semisupervised learning and conditional random fields (2011)
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- Abstract
- Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.
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Chen, H.; Dhar, V.: Cognitive process as a basis for intelligent retrieval system design (1991)
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- Abstract
- 2 studies were conducted to investigate the cognitive processes involved in online document-based information retrieval. These studies led to the development of 5 computerised models of online document retrieval. These models were incorporated into a design of an 'intelligent' document-based retrieval system. Following a discussion of this system, discusses the broader implications of the research for the design of information retrieval sysems
- Source
- Information processing and management. 27(1991) no.5, S.405-432
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Schumaker, R.P.; Chen, H.: Evaluating a news-aware quantitative trader : the effect of momentum and contrarian stock selection strategies (2008)
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- Abstract
- We study the coupling of basic quantitative portfolio selection strategies with a financial news article prediction system, AZFinText. By varying the degrees of portfolio formation time, we found that a hybrid system using both quantitative strategy and a full set of financial news articles performed the best. With a 1-week portfolio formation period, we achieved a 20.79% trading return using a Momentum strategy and a 4.54% return using a Contrarian strategy over a 5-week holding period. We also found that trader overreaction to these events led AZFinText to capitalize on these short-term surges in price.
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Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998)
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- Date
- 5. 4.1996 20:01:47
-
Chen, H.: Semantic research for digital libraries (1999)
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- Source
- D-Lib magazine. 5(1999) no.10, xx S
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Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008)
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- Source
- Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.756-769
-
Ku, Y.; Chiu, C.; Zhang, Y.; Chen, H.; Su, H.: Text mining self-disclosing health information for public health service (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.5, S.928-947
-
Jiang, S.; Gao, Q.; Chen, H.; Roco, M.C.: ¬The roles of sharing, transfer, and public funding in nanotechnology knowledge-diffusion networks (2015)
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- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.5, S.1017-1029
-
Qin, J.; Zhou, Y.; Chau, M.; Chen, H.: Multilingual Web retrieval : an experiment in English-Chinese business intelligence (2006)
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- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.671-683
