Abstract
This paper presents a novel method for acquiring a set of query patterns to retrieve documents containing important information about an entity. Given an existing Wikipedia category that contains the target entity, we extract and select a small set of query patterns by presuming that formulating search queries with these patterns optimizes the overall precision and coverage of the returned Web information. We model this optimization problem as a weighted maximum satisfiability (weighted Max-SAT) problem. The experimental results demonstrate that the proposed method outperforms other methods based on statistical measures such as frequency and point-wise mutual information (PMI), which are widely used in relation extraction.
| Original language | English |
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| Title of host publication | Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference |
| Pages | 1229-1237 |
| Number of pages | 9 |
| Volume | 2 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 23rd International Conference on Computational Linguistics, Coling 2010 - Beijing Duration: 2010 Aug 23 → 2010 Aug 27 |
Other
| Other | 23rd International Conference on Computational Linguistics, Coling 2010 |
|---|---|
| City | Beijing |
| Period | 10/8/23 → 10/8/27 |
ASJC Scopus subject areas
- Language and Linguistics
- Computational Theory and Mathematics
- Linguistics and Language