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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105835 |
| Date | January 2007 |
| Creators | Wu, Paul Horng Jyh, Na, Jin Cheon, Khoo, Christopher S.G. |
| Publisher | SAGE Publications |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (On-line/Unpaginated) |
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