• Behavioural complexity theory of media selection: A proposed theory for global virtual teams

      Shachaf, Pnina; Hara, Noriko (2007)
      This study proposes a behavioural complexity theory for media selection in global virtual teams. This theory captures multiple contingencies into one holistic approach to media selection. Unlike existing linear and mechanistic theories of media selection, this heuristic theory moves away from the universal models that were previously proposed. The behavioural complexity theory assumes ambiguity and complexity of the media selection process in a nonlinear, organic, and holistic way. Behavioural complexity theory of media selection emphasizes the role of media repertoire, the ability of individuals to differentiate situations according to multiple contingencies, and their flexibility to effectively use multiple media in any particular situation. This theory is examined in a context of exploratory case study of global virtual teamsâ media selection in one of the leading fortune 500 corporations.
    • A hybrid approach to fuzzy name search incorporating language-based and textbased principles

      Wu, Paul Horng Jyh; Na, Jin Cheon; Khoo, Christopher S.G. (SAGE Publications, 2007)
      Name Search is an important search function in various types of information retrieval systems, such as online library catalogs and electronic yellow pages. It is also difficult due to the high degree of fuzziness required in matching name variants. Previous approaches to name search systems use ad hoc combinations of search heuristics. This paper first discusses two approaches to name modelingâ the natural language processing (NLP) and the information retrieval (IR) modelsâ and proposes a hybrid approach. The approach demonstrates a critical combination of complementary NLP and IR features that produces more effective fuzzy name matching. Two principles, position-as-attribute and position-transitionlikelihood, are introduced as the principles for integrating the advantageous aspects of both approaches. They have been implemented in an NLP- and IR- hybrid model system called Friendly Name Search (FNS) for real world applications in multilingual directory searches on the Singapore Yellow pages website.
    • Semantic Indexing and Searching Using a Hopfield Net

      Chen, Hsinchun; Zhang, Yin; Houston, Andrea L. (1998)
      This paper presents a neural network approach to document semantic indexing. A Hopfield net algorithm was used to simulate human associative memory for concept exploration in the domain of computer science and engineering. INSPEC, a collection of more than 320,000 document abstracts from leading journals, was used as the document testbed. Benchmark tests confirmed that three parameters (maximum number of activated nodes, E - maximum allowable error, and maximum number of iterations) were useful in positively influencing network convergence behavior without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests confirmed our expectation that the Hopfield net algorithm is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end-user vocabularies.