Semantic integration in learning from text

Steven Bethard, Rodney Nielsen, James H. Martin, Wayne Ward, Martha Palmer

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

2 Scopus citations

Abstract

We define learning as the generation of meaningful knowledge representations which can be utilized in future decision making. Optimal learning entails that these knowledge representations be integrated with prior knowledge. In this paper, we introduce a knowledge representation based on an integration of a variety of shallow semantic parsing techniques. Entity detection, event detection, semantic role labeling and temporal relation identification are combined to produce graph-like structures which represent the most important semantic components of a text and the relations between these components. We show how new entities, events and relations can be successfully integrated into this representation using features derived from lexical and dependency-based sources.

Original languageEnglish (US)
Title of host publicationMachine Reading - Papers from the 2007 AAAI Spring Symposium, Technical Report
Pages17-22
Number of pages6
StatePublished - 2007
Externally publishedYes
Event2007 AAAI Spring Symposium - Stanford, CA, United States
Duration: Mar 26 2007Mar 28 2007

Publication series

NameAAAI Spring Symposium - Technical Report
VolumeSS-07-06

Conference

Conference2007 AAAI Spring Symposium
Country/TerritoryUnited States
CityStanford, CA
Period3/26/073/28/07

ASJC Scopus subject areas

  • Engineering(all)

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