Name
Abstract | ||
|---|---|---|
Reflecting the rapid growth of science, technology, and culture, it has become common practice to consult tools on the World Wide Web for various terms. Existing search engines provide an enormous volume of information, but retrieved information is not organized. Hand-compiled encyclopedias provide organized information, but the quantity of information is limited. In this paper, aiming to integrate the advantages of both tools, we propose a method to organize a search result based on multiple viewpoints as in Wikipedia. Because viewpoints required for explanation are different depending on the type of a term, such as animal and disease, we model articles in Wikipedia to extract a viewpoint structure for each term type. To identify a set of term types, we independently use manual annotation and automatic document clustering for Wikipedia articles. We also propose an effective feature for clustering of Wikipedia articles. We experimentally show that the document clustering reduces the cost for the manual annotation while maintaining the accuracy for modeling Wikipedia articles. |
Year | Venue | Keywords |
|---|---|---|
2012 | LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION | Term description,Wikipedia,Clustering |
Field | DocType | Citations |
Search engine,Document clustering,Computer science,Viewpoints,Manual annotation,Artificial intelligence,Encyclopedia,Natural language processing,Cluster analysis | Conference | 1 |
PageRank | References | Authors |
0.36 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atsushi Fujii | 1 | 486 | 59.25 |
| Yuya Fujii | 2 | 1 | 0.36 |
| Takenobu Tokunaga | 3 | 560 | 82.66 |