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Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective
| dc.contributor.author | Chen, Hsinchun | |
| dc.date.accessioned | 2004-08-16T00:00:01Z | |
| dc.date.available | 2010-06-18T23:23:41Z | |
| dc.date.issued | 2003-05 | en_US |
| dc.date.submitted | 2004-08-16 | en_US |
| dc.identifier.citation | Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective 2003-05, 54(7):621-624 Journal of the American Society for Information Science & Technology | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105320 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.abstract | 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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Wiley Periodicals, Inc | en_US |
| dc.subject | Web Mining | en_US |
| dc.subject | World Wide Web | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial Intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.subject.other | Information retrieval | en_US |
| dc.subject.other | Machine learning | en_US |
| dc.title | Introduction to the JASIST Special Topic Section on Web Retrieval and Mining: A Machine Learning Perspective | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | Journal of the American Society for Information Science & Technology | en_US |
| refterms.dateFOA | 2018-08-21T11:28:28Z | |
| html.description.abstract | 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. |
