Summary of the paper
| Title | Modeling Wikipedia Articles to Enhance Encyclopedic Search |
|---|---|
| Authors | Atsushi Fujii |
| Abstract | Reflecting the rapid growth of science, technology, and culture, it has becomecommon practice to consult tools on the World Wide Web for various terms.Existing search engines provide an enormous volume of information, butretrieved information is not organized. Hand-compiled encyclopedias provideorganized information, but the quantity of information is limited. To integratethe advantages of both tools, we have been proposing methods for encyclopedicsearch targeting information on the Web and patent information. In this paper,we propose a method to categorize multiple expository texts for a single termbased on viewpoints. Because viewpoints required for explanation are differentdepending on the type of a term, such as animals and diseases, it is difficultto manually produce a large scale system. We use Wikipedia to extract aprototype of a viewpoint structure for each term type. We also use articles inWikipedia for a machine learning method, which categorizes a given text into anappropriate viewpoint. We evaluate the effectiveness of our methodexperimentally. |
| Language | Summarisation |
| Topics | Information Extraction, Information Retrieval, Document Classification, Text categorisation, Summarisation |
Full paper  |
Modeling Wikipedia Articles to Enhance Encyclopedic Search |
| Bibtex | @InProceedings{FUJII10.684, author = {Atsushi Fujii}, title = {Modeling Wikipedia Articles to Enhance Encyclopedic Search}, booktitle = {Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10)}, year = {2010}, month = {may}, date = {19-21}, address = {Valletta, Malta}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odjik, Stelios Piperidis, Mike Rosner, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-6-7}, language = {english} } |