Graph based multi-view learning for CDL relation classification

Haibo Li; Yutaka Matsuo; Mitsuru Ishizuka

doi:10.1109/ICSC.2009.97

Graph based multi-view learning for CDL relation classification

Haibo Li^*, Yutaka Matsuo, Mitsuru Ishizuka

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	ICSC 2009 - 2009 IEEE International Conference on Semantic Computing
Pages	473-480
Number of pages	8
DOIs	https://doi.org/10.1109/ICSC.2009.97
Publication status	Published - 2009
Externally published	Yes
Event	ICSC 2009 - 2009 IEEE International Conference on Semantic Computing - Berkeley, CA Duration: 2009 Sept 14 → 2009 Sept 16

Other

Other	ICSC 2009 - 2009 IEEE International Conference on Semantic Computing
City	Berkeley, CA
Period	09/9/14 → 09/9/16

Keywords

CDL
Graph based model
Multi-view learning
Relation classification
Semi-supervised learning

ASJC Scopus subject areas

Computational Theory and Mathematics
Software
Electrical and Electronic Engineering

Access to Document

10.1109/ICSC.2009.97

Cite this

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title = "Graph based multi-view learning for CDL relation classification",

abstract = "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.",

keywords = "CDL, Graph based model, Multi-view learning, Relation classification, Semi-supervised learning",

author = "Haibo Li and Yutaka Matsuo and Mitsuru Ishizuka",

year = "2009",

doi = "10.1109/ICSC.2009.97",

language = "English",

isbn = "9780769538006",

pages = "473--480",

booktitle = "ICSC 2009 - 2009 IEEE International Conference on Semantic Computing",

note = "ICSC 2009 - 2009 IEEE International Conference on Semantic Computing ; Conference date: 14-09-2009 Through 16-09-2009",

}

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

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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

T2 - ICSC 2009 - 2009 IEEE International Conference on Semantic Computing

Y2 - 14 September 2009 through 16 September 2009

ER -

Graph based multi-view learning for CDL relation classification

Abstract

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Keywords

ASJC Scopus subject areas

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