Artificial Intelligence Lab, Department of MIS, University of Arizona / Research in information retrieval (IR) has advanced significantly
in the past few decades. Many tasks, such as
indexing and text categorization, can be performed automatically
with minimal human effort. Machine learning has
played an important role in such automation by learning
various patterns such as document topics, text structures,
and user interests from examples.
In recent years, it has become increasingly difficult to
search for useful information on the World Wide Web
because of its large size and unstructured nature. Useful
information and resources are often hidden in the Web.
While machine learning has been successfully applied to
traditional IR systems, it poses some new challenges to
apply these algorithms to the Web due to its large size, link
structure, diversity in content and languages, and dynamic
nature. On the other hand, such characteristics of the Web
also provide interesting patterns and knowledge that do not
present in traditional information retrieval systems.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105320 |
| Date | 05 1900 |
| Creators | Chen, Hsinchun |
| Publisher | Wiley Periodicals, Inc |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
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