Search (37 results, page 1 of 2)

  • × author_ss:"Chen, H."
  1. Leroy, G.; Chen, H.: Genescene: an ontology-enhanced integration of linguistic and co-occurrence based relations in biomedical texts (2005) 0.04 
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    Abstract
    The increasing amount of publicly available literature and experimental data in biomedicine makes it hard for biomedical researchers to stay up-to-date. Genescene is a toolkit that will help alleviate this problem by providing an overview of published literature content. We combined a linguistic parser with Concept Space, a co-occurrence based semantic net. Both techniques extract complementary biomedical relations between noun phrases from MEDLINE abstracts. The parser extracts precise and semantically rich relations from individual abstracts. Concept Space extracts relations that hold true for the collection of abstracts. The Gene Ontology, the Human Genome Nomenclature, and the Unified Medical Language System, are also integrated in Genescene. Currently, they are used to facilitate the integration of the two relation types, and to select the more interesting and high-quality relations for presentation. A user study focusing on p53 literature is discussed. All MEDLINE abstracts discussing p53 were processed in Genescene. Two researchers evaluated the terms and relations from several abstracts of interest to them. The results show that the terms were precise (precision 93%) and relevant, as were the parser relations (precision 95%). The Concept Space relations were more precise when selected with ontological knowledge (precision 78%) than without (60%).
    Date
    22. 7.2006 14:26:01
  2. Carmel, E.; Crawford, S.; Chen, H.: Browsing in hypertext : a cognitive study (1992) 0.03 
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    Abstract
    With the growth of hypertext and multimedia applications that support and encourage browsing it is time to take a penetrating look at browsing behaviour. Several dimensions of browsing are exemined, to find out: first, what is browsing and what cognitive processes are associated with it: second, is there a browsing strategy, and if so, are there any differences between how subject-area experts and novices browse; and finally, how can this knowledge be applied to improve the design of hypertext systems. Two groups of students, subject-area experts and novices, were studied while browsing a Macintosh HyperCard application on the subject The Vietnam War. A protocol analysis technique was used to gather and analyze data. Components of the GOMS model were used to describe the goals, operators, methods, and selection rules observed: Three browsing strategies were identified: (1) search-oriented browse, scanning and and reviewing information relevant to a fixed task; (2) review-browse, scanning and reviewing intersting information in the presence of transient browse goals that represent changing tasks, and (3) scan-browse, scanning for interesting information (without review). Most subjects primarily used review-browse interspersed with search-oriented browse. Within this strategy, comparisons between subject-area experts and novices revealed differences in tactics: experts browsed in more depth, seldom used referential links, selected different kinds of topics, and viewed information differently thatn did novices. Based on these findings, suggestions are made to hypertext developers
    Source
    IEEE transactions on systems, man and cybernetics. 22(1992) no.5, S.865-884
  3. Zheng, R.; Li, J.; Chen, H.; Huang, Z.: ¬A framework for authorship identification of online messages : writing-style features and classification techniques (2006) 0.03 
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    Abstract
    With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.
    Date
    22. 7.2006 16:14:37
  4. Chen, H.: Knowledge-based document retrieval : framework and design (1992) 0.03 
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  5. Chen, H.: Generating, integrating and activating thesauri for concept-based document retrieval (1993) 0.03 
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  6. Chen, H.; Lynch, K.J.; Bashu, K.; Ng, T.D.: Generating, integrating, and activating thesauri for concept-based document retrieval (1993) 0.03 
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  7. Chen, H.: Machine learning for information retrieval : neural networks, symbolic learning, and genetic algorithms (1994) 0.03 
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    Abstract
    In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques
  8. 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) 0.02 
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    Abstract
    Presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge based systems and to alleviate the limitation of the manual browsing approach, develops 2 spreading activation based algorithms for concept exploration in large, heterogeneous networks of concepts (eg multiple thesauri). One algorithm, which is based on the symbolic AI paradigma, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The 2nd algorithm, which is absed on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify 'convergent' concepts for some initial queries (a parallel, heuristic search process). Tests these 2 algorithms on a large text-based knowledge network of about 13.000 nodes (terms) and 80.000 directed links in the area of computing technologies
  9. Schroeder, J.; Xu, J.; Chen, H.; Chau, M.: Automated criminal link analysis based on domain knowledge (2007) 0.02 
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    Abstract
    Link (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.
  10. Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.02 
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    Abstract
    Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.
  11. Chen, H.; Dhar, V.: Cognitive process as a basis for intelligent retrieval system design (1991) 0.02 
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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
  12. Chen, H.: Explaining and alleviating information management indeterminism : a knowledge-based framework (1994) 0.02 
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    Abstract
    Attempts to identify the nature and causes of information management indeterminism in an online research environment and proposes solutions for alleviating this indeterminism. Conducts two empirical studies of information management activities. The first identified the types and nature of information management indeterminism by evaluating archived text. The second focused on four sources of indeterminism: subject area knowledge, classification knowledge, system knowledge, and collaboration knowledge. Proposes a knowledge based design for alleviating indeterminism, which contains a system generated thesaurus and an inferencing engine
  13. Chen, H.; Chung, Y.-M.; Ramsey, M.; Yang, C.C.: ¬A smart itsy bitsy spider for the Web (1998) 0.02 
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    Abstract
    As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent agent approach to Web searching. In this experiment, we developed 2 Web personal spiders based on best first search and genetic algorithm techniques, respectively. These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the Web, based on the links and keyword indexing. A graphical, dynamic, Jav-based interface was developed and is available for Web access. A system architecture for implementing such an agent-spider is presented, followed by deteiled discussions of benchmark testing and user evaluation results. In benchmark testing, although the genetic algorithm spider did not outperform the best first search spider, we found both results to be comparable and complementary. In user evaluation, the genetic algorithm spider obtained significantly higher recall value than that of the best first search spider. However, their precision values were not statistically different. The mutation process introduced in genetic algorithms allows users to find other potential relevant homepages that cannot be explored via a conventional local search process. In addition, we found the Java-based interface to be a necessary component for design of a truly interactive and dynamic Web agent
  14. Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010) 0.02 
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    Abstract
    With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.
  15. Marshall, B.; Chen, H.; Kaza, S.: Using importance flooding to identify interesting networks of criminal activity (2008) 0.01 
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    Abstract
    Effectively harnessing available data to support homeland-security-related applications is a major focus in the emerging science of intelligence and security informatics (ISI). Many studies have focused on criminal-network analysis as a major challenge within the ISI domain. Though various methodologies have been proposed, none have been tested for usefulness in creating link charts. This study compares manually created link charts to suggestions made by the proposed importance-flooding algorithm. Mirroring manual investigational processes, our iterative computation employs association-strength metrics, incorporates path-based node importance heuristics, allows for case-specific notions of importance, and adjusts based on the accuracy of previous suggestions. Interesting items are identified by leveraging both node attributes and network structure in a single computation. Our data set was systematically constructed from heterogeneous sources and omits many privacy-sensitive data elements such as case narratives and phone numbers. The flooding algorithm improved on both manual and link-weight-only computations, and our results suggest that the approach is robust across different interpretations of the user-provided heuristics. This study demonstrates an interesting methodology for including user-provided heuristics in network-based analysis, and can help guide the development of ISI-related analysis tools.
  16. Chen, H.; Shankaranarayanan, G.; She, L.: ¬A machine learning approach to inductive query by examples : an experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing (1998) 0.01 
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    Abstract
    Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, information science researchers have tfurned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to femiliarize readers with the techniques, we present 3 promising methods: the symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval
  17. 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) 0.01 
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    Abstract
    In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurence analysis, we performed a large-scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400.000+ abstracts in an INSPEC computer engineering collection. Two system-generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compaed with the human-generated INSPEC subject thesaurus. Our user evaluation revealed that the system-generated thesauri were better than the INSPEC thesaurus in 'concept recall', but in 'concept precision' the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase 'variety' in search terms the thereby reduce search uncertainty
  18. Fu, T.; Abbasi, A.; Chen, H.: ¬A hybrid approach to Web forum interactional coherence analysis (2008) 0.01 
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    Abstract
    Despite the rapid growth of text-based computer-mediated communication (CMC), its limitations have rendered the media highly incoherent. This poses problems for content analysis of online discourse archives. Interactional coherence analysis (ICA) attempts to accurately identify and construct CMC interaction networks. In this study, we propose the Hybrid Interactional Coherence (HIC) algorithm for identification of web forum interaction. HIC utilizes a bevy of system and linguistic features, including message header information, quotations, direct address, and lexical relations. Furthermore, several similarity-based methods including a Lexical Match Algorithm (LMA) and a sliding window method are utilized to account for interactional idiosyncrasies. Experiments results on two web forums revealed that the proposed HIC algorithm significantly outperformed comparison techniques in terms of precision, recall, and F-measure at both the forum and thread levels. Additionally, an example was used to illustrate how the improved ICA results can facilitate enhanced social network and role analysis capabilities.
  19. 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) 0.01 
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    Abstract
    This research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabulary-based search aids (thesauri) and then discuss techniques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7.657 worm-specific terms and a Drosophila fly thesaurus of 15.626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitve study of term association involving 4 biologists, we found that a large percentage (59,6-85,6%) of the terms suggested by the subjects were identified in the cojoined fly-worm thesaurus. However, we found only a small percentage (8,4-18,1%) of the associations suggested by the subjects in the thesaurus
  20. Qin, J.; Zhou, Y.; Chau, M.; Chen, H.: Multilingual Web retrieval : an experiment in English-Chinese business intelligence (2006) 0.01 
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    Abstract
    As increasing numbers of non-English resources have become available on the Web, the interesting and important issue of how Web users can retrieve documents in different languages has arisen. Cross-language information retrieval (CLIP), the study of retrieving information in one language by queries expressed in another language, is a promising approach to the problem. Cross-language information retrieval has attracted much attention in recent years. Most research systems have achieved satisfactory performance on standard Text REtrieval Conference (TREC) collections such as news articles, but CLIR techniques have not been widely studied and evaluated for applications such as Web portals. In this article, the authors present their research in developing and evaluating a multilingual English-Chinese Web portal that incorporates various CLIP techniques for use in the business domain. A dictionary-based approach was adopted and combines phrasal translation, co-occurrence analysis, and pre- and posttranslation query expansion. The portal was evaluated by domain experts, using a set of queries in both English and Chinese. The experimental results showed that co-occurrence-based phrasal translation achieved a 74.6% improvement in precision over simple word-byword translation. When used together, pre- and posttranslation query expansion improved the performance slightly, achieving a 78.0% improvement over the baseline word-by-word translation approach. In general, applying CLIR techniques in Web applications shows promise.

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