抄録
To understand text contents better, many research efforts have been made exploring detection and classification of the semantic relation between a concept pair. As described herein, we present our study of a semantic relation classification task as a graph-based multi-view learning task: each intra-view graph is constructed with instances in the view; a node's label "score" is propagated on each intra-view graph and inter-view graph. This combination of multi-view learning and graph-based method can reduce the influence from violation of a background assumption of multi-view learning algorithms - view compatibility. The proposed algorithm is evaluated using the Concept Description Language for Natural Language (CDL.nl) corpus. The experiment results validate its effectiveness.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | ICSC 2009 - 2009 IEEE International Conference on Semantic Computing |
| ページ | 473-480 |
| ページ数 | 8 |
| DOI | |
| 出版物ステータス | Published - 2009 |
| 外部発表 | Yes |
| イベント | ICSC 2009 - 2009 IEEE International Conference on Semantic Computing - Berkeley, CA 継続期間: 2009 9 14 → 2009 9 16 |
Other
| Other | ICSC 2009 - 2009 IEEE International Conference on Semantic Computing |
|---|---|
| 市 | Berkeley, CA |
| 期間 | 09/9/14 → 09/9/16 |
Fingerprint
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Software
- Electrical and Electronic Engineering
これを引用
Graph based multi-view learning for CDL relation classification. / Li, Haibo; Matsuo, Yutaka; Ishizuka, Mitsuru.
ICSC 2009 - 2009 IEEE International Conference on Semantic Computing. 2009. p. 473-480 5298632.研究成果: Conference contribution
}
TY - GEN
T1 - Graph based multi-view learning for CDL relation classification
AU - Li, Haibo
AU - Matsuo, Yutaka
AU - Ishizuka, Mitsuru
PY - 2009
Y1 - 2009
N2 - To understand text contents better, many research efforts have been made exploring detection and classification of the semantic relation between a concept pair. As described herein, we present our study of a semantic relation classification task as a graph-based multi-view learning task: each intra-view graph is constructed with instances in the view; a node's label "score" is propagated on each intra-view graph and inter-view graph. This combination of multi-view learning and graph-based method can reduce the influence from violation of a background assumption of multi-view learning algorithms - view compatibility. The proposed algorithm is evaluated using the Concept Description Language for Natural Language (CDL.nl) corpus. The experiment results validate its effectiveness.
AB - To understand text contents better, many research efforts have been made exploring detection and classification of the semantic relation between a concept pair. As described herein, we present our study of a semantic relation classification task as a graph-based multi-view learning task: each intra-view graph is constructed with instances in the view; a node's label "score" is propagated on each intra-view graph and inter-view graph. This combination of multi-view learning and graph-based method can reduce the influence from violation of a background assumption of multi-view learning algorithms - view compatibility. The proposed algorithm is evaluated using the Concept Description Language for Natural Language (CDL.nl) corpus. The experiment results validate its effectiveness.
KW - CDL
KW - Graph based model
KW - Multi-view learning
KW - Relation classification
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=73449088706&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73449088706&partnerID=8YFLogxK
U2 - 10.1109/ICSC.2009.97
DO - 10.1109/ICSC.2009.97
M3 - Conference contribution
AN - SCOPUS:73449088706
SN - 9780769538006
SP - 473
EP - 480
BT - ICSC 2009 - 2009 IEEE International Conference on Semantic Computing
ER -