Abstract
Information extraction systems automatically extract structured information from machine-readable documents, such as newswire, web, and multimedia. Despite significant improvement, the performance is far from perfect. Hence, it is useful to accurately estimate confidence in the correctness of the extracted information. Using the Knowledge Base Population Slot Filling task as a case study, we propose a confidence estimation model based on the Maximum Entropy framework, obtaining an average precision of 83.5%, Pearson coefficient of 54.2%, and 2.3% absolute improvement in F-measure score through a weighted voting strategy.
| Original language | English (US) |
|---|---|
| Title of host publication | International Conference Recent Advances in Natural Language Processing, RANLP |
| Pages | 396-401 |
| Number of pages | 6 |
| State | Published - 2013 |
| Event | 9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 - Hissar, Bulgaria Duration: Sep 9 2013 → Sep 11 2013 |
Other
| Other | 9th International Conference on Recent Advances in Natural Language Processing, RANLP 2013 |
|---|---|
| Country/Territory | Bulgaria |
| City | Hissar |
| Period | 9/9/13 → 9/11/13 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Software
- Electrical and Electronic Engineering