Using SVMs for Classification of Cross-Document Relationships
Cross-document Structure Theory (CST) has recently been proposed to facilitate tasks related to multi-document analysis. Classifying and identifying the CST relationships between sentences across topically related documents have since been proven as necessary. However, there have not been sufficient...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University Putra Malaysia Press
2013
|
| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/6707/1/JST-0000-2012_YOGAN_JAYA_KUMAR_%28ORG_MS%29_22_August_2012_.pdf http://eprints.utem.edu.my/id/eprint/6707/ http://www.pertanika.upm.edu.my/JST.php |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Cross-document Structure Theory (CST) has recently been proposed to facilitate tasks related to multi-document analysis. Classifying and identifying the CST relationships between sentences across topically related documents have since been proven as necessary. However, there have not been sufficient studies presented in literature to automatically identify these CST relationships. In this study, a supervised machine learning technique, i.e. Support Vector Machines (SVMs), was applied to identify four types of CST relationships, namely “Identity”, “Overlap”, “Subsumption”, and “Description” on the datasets obtained from CSTBank corpus. The performance of the SVMs classification was measured using Precision, Recall and F-measure. In addition, the results obtained using SVMs were also compared with those from the previous literature using boosting classification algorithm. It was found that SVMs yielded better results in classifying the four CST relationships. |
|---|