A framework for integrating deep and shallow semantic structures in text mining

Nigel Collier, Koichi Takeuchi, Ai Kawazoe, Tony Mullen, Tuangthong Wattarujeekrit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Recent work in knowledge representation undertaken as part of the Semantic Web initiative has enabled a common infrastructure (Resource Description Framework (RDF) and RDF Schema) for sharing knowledge of ontologies and instances. In this paper we present a framework for combining the shallow levels of semantic description commonly used in MUC-style information extraction with the deeper semantic structures available in such ontologies. The framework is implemented within the PIA project software called Ontology Forge. Ontology Forge offers a server-based hosting environment for ontologies, a server-side information extraction system for reducing the effort of writing annotations and a many-featured ontology/annotation editor. We discuss the knowledge framework, some features of the system and summarize results from extended named entity experiments designed to capture instances in texts using support vector machine software.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsV. Palade, R.J. Howlett, L. Jain
Pages824-834
Number of pages11
Volume2773 PART 1
Publication statusPublished - 2003
Externally publishedYes
Event7th International Conference, KES 2003 - Oxford, United Kingdom
Duration: Sep 3 2003Sep 5 2003

Other

Other7th International Conference, KES 2003
CountryUnited Kingdom
CityOxford
Period9/3/039/5/03

Fingerprint

Text Mining
Ontology
Semantics
Information Extraction
Annotation
Servers
Server
Resources
Knowledge Sharing
Software
Side Information
Knowledge representation
Knowledge Representation
Semantic Web
Schema
Support vector machines
Framework
Support Vector Machine
Computer systems
Infrastructure

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Collier, N., Takeuchi, K., Kawazoe, A., Mullen, T., & Wattarujeekrit, T. (2003). A framework for integrating deep and shallow semantic structures in text mining. In V. Palade, R. J. Howlett, & L. Jain (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 824-834)

A framework for integrating deep and shallow semantic structures in text mining. / Collier, Nigel; Takeuchi, Koichi; Kawazoe, Ai; Mullen, Tony; Wattarujeekrit, Tuangthong.

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). ed. / V. Palade; R.J. Howlett; L. Jain. Vol. 2773 PART 1 2003. p. 824-834.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Collier, N, Takeuchi, K, Kawazoe, A, Mullen, T & Wattarujeekrit, T 2003, A framework for integrating deep and shallow semantic structures in text mining. in V Palade, RJ Howlett & L Jain (eds), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). vol. 2773 PART 1, pp. 824-834, 7th International Conference, KES 2003, Oxford, United Kingdom, 9/3/03.
Collier N, Takeuchi K, Kawazoe A, Mullen T, Wattarujeekrit T. A framework for integrating deep and shallow semantic structures in text mining. In Palade V, Howlett RJ, Jain L, editors, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). Vol. 2773 PART 1. 2003. p. 824-834
Collier, Nigel ; Takeuchi, Koichi ; Kawazoe, Ai ; Mullen, Tony ; Wattarujeekrit, Tuangthong. / A framework for integrating deep and shallow semantic structures in text mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). editor / V. Palade ; R.J. Howlett ; L. Jain. Vol. 2773 PART 1 2003. pp. 824-834
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