Relation classification for semantic structure annotation of text

Yulan Yan; Yutaka Matsuo; Mitsuru Ishizuka; Toshio Yokoi

doi:10.1109/WIIAT.2008.128

Relation classification for semantic structure annotation of text

Yulan Yan^*, Yutaka Matsuo, Mitsuru Ishizuka, Toshio Yokoi

^*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

Confronting the challenges of annotating naturally occurring text into a semantically structured form to facilitate automatic information extraction, current Semantic Role Labeling (SRL) systems have been specifically examining a semantic predicate-argument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which is intended to describe the concept structure of text using a set of pre-defined semantic relations, we develop a parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. With the assumption that all relation instances are detected, we present a relation classification approach facing the challenges of CDL.nl relation extraction. Preliminary evaluation on a manual dataset, using Support Vector Machine, shows that CDL.nl relations can be classified with good performance.

Original language	English
Title of host publication	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Pages	377-380
Number of pages	4
DOIs	https://doi.org/10.1109/WIIAT.2008.128
Publication status	Published - 2008
Externally published	Yes
Event	2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW Duration: 2008 Dec 9 → 2008 Dec 12

Other

Other	2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
City	Sydney, NSW
Period	08/12/9 → 08/12/12

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/WIIAT.2008.128

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title = "Relation classification for semantic structure annotation of text",

abstract = "Confronting the challenges of annotating naturally occurring text into a semantically structured form to facilitate automatic information extraction, current Semantic Role Labeling (SRL) systems have been specifically examining a semantic predicate-argument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which is intended to describe the concept structure of text using a set of pre-defined semantic relations, we develop a parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. With the assumption that all relation instances are detected, we present a relation classification approach facing the challenges of CDL.nl relation extraction. Preliminary evaluation on a manual dataset, using Support Vector Machine, shows that CDL.nl relations can be classified with good performance.",

author = "Yulan Yan and Yutaka Matsuo and Mitsuru Ishizuka and Toshio Yokoi",

year = "2008",

doi = "10.1109/WIIAT.2008.128",

language = "English",

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pages = "377--380",

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note = "2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 ; Conference date: 09-12-2008 Through 12-12-2008",

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AU - Yan, Yulan

AU - Matsuo, Yutaka

AU - Ishizuka, Mitsuru

AU - Yokoi, Toshio

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AB - Confronting the challenges of annotating naturally occurring text into a semantically structured form to facilitate automatic information extraction, current Semantic Role Labeling (SRL) systems have been specifically examining a semantic predicate-argument structure. Based on the Concept Description Language for Natural Language (CDL.nl) which is intended to describe the concept structure of text using a set of pre-defined semantic relations, we develop a parser to add a new layer of semantic annotation of natural language sentences as an extension of SRL. With the assumption that all relation instances are detected, we present a relation classification approach facing the challenges of CDL.nl relation extraction. Preliminary evaluation on a manual dataset, using Support Vector Machine, shows that CDL.nl relations can be classified with good performance.

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DO - 10.1109/WIIAT.2008.128

M3 - Conference contribution

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BT - Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

T2 - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

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ER -

Relation classification for semantic structure annotation of text

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