Lexico-syntactic causal pattern text mining

Authors:
Sonali Joshi;Manali Pangaonkar;Swathi Seethakkagari;Lawrence J. Mazlack
Affiliations:
Applied Artificial Intelligence Laboratory, University of Cincinnati, Cincinnati, Ohio;Applied Artificial Intelligence Laboratory, University of Cincinnati, Cincinnati, Ohio;Applied Artificial Intelligence Laboratory, University of Cincinnati, Cincinnati, Ohio;Applied Artificial Intelligence Laboratory, University of Cincinnati, Cincinnati, Ohio
Venue:
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Year:
2010

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Abstract

The major aspect of text mining is to capture essential information in the text. Of particular information is the discovery of causality. In a text, causality comes in various forms or patterns. To understand the causal relations in the text, we first need to know which all causation patterns are present. The long term goal is to mine text for causal patterns. Our focus is on semi-automatic generation of causation patterns. The objective of this research is to understand the important information in the text that is in the form of causal relations. The central hypothesis of this research is that every causal relation can be expressed in the form a lexico-syntactic pattern. We have considered marked and explicit causations.