Name
Abstract | ||
|---|---|---|
Effective inference of user interests is crucial to personalization. Utilizing the Open Directory Project (ODP) categories is an effective way to infer user interests, which represents user interests in the form of ODP categories, i.e., nouns. In this paper, we build a knowledge base to represent user interests in the form of (noun, verb) pairs. We expect that this approach will enable us to represent user interests more precisely, since verbs clarify the context of nouns. To this end, we develop a verb extraction engine that extends ODP categories with their related verbs. It employs various information sources to automatically identify a set of related verbs for an arbitrary ODP category. Thus, we obtain the extended ODP categories in the form of (noun, verb) pairs that will be utilized for various personalization services. The experimental results show the efficacy of our verb extraction engine. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2245276.2245345 | SAC |
Keywords | Field | DocType |
effective inference,extending open directory project,various information source,verb extraction engine,arbitrary odp category,odp category,user interest,open directory project,various personalization service,extended odp category,related verb,noun,personalization,knowledge base | Verb,Computer science,Directory,Inference,Noun,Natural language processing,Artificial intelligence,Knowledge base,Personalization | Conference |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seulgi So | 1 | 0 | 0.34 |
| Jung-Hyun Lee | 2 | 188 | 23.59 |
| Daoun Jung | 3 | 0 | 0.34 |
| JongWoo Ha | 4 | 55 | 6.79 |
| Sangkeun Lee | 5 | 498 | 65.59 |