Name
Abstract | ||
|---|---|---|
This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the jobs corpus, and is second best on the remaining nine. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/11559887_19 | Deterministic and Statistical Methods in Machine Learning |
Keywords | Field | DocType |
cmu seminar,statistical learning algorithm,state-of-the-art system,fewer svm classifier,software jobs corpus,ie benchmark datasets,svm-based learning system,recent svm-based system,small training datasets,information extraction,jobs corpus | Ranking SVM,Pattern recognition,Computer science,Support vector machine,Information extraction,Software,Statistical learning,Artificial intelligence,Distinctive feature,Support vector machine algorithm,Machine learning | Conference |
Volume | ISSN | ISBN |
3635 | 0302-9743 | 3-540-29073-7 |
Citations | PageRank | References |
37 | 2.15 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoyong Li | 1 | 393 | 26.55 |
| Kalina Bontcheva | 2 | 2538 | 211.33 |
| Hamish Cunningham | 3 | 2426 | 255.41 |