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An Open-Domain Cause-Effect Relation Detection from Paired Nominals

  • Partha Pakray
  • Alexander Gelbukh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8857)

Abstract

We present a supervised method for detecting causal relations from text. Various kinds of dependency relations, WordNet features, Parts-of-Speech (POS) features along with several combinations of these features help to improve the performance of our system. In our experiments, we used SemEval-2010 Task #8 data sets. This system used 7954 instances for training and 2707 instances for testing from Task #8 datasets. The J48 algorithm was used to identify semantic causal relations in a pair of nominals. Evaluation result gives an overall F1 score of 85.8% of causal instances.

Keywords

Causal Relation Noun Phrase Semantic Relation Natural Language Text Receiver Operating Characteristic Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Partha Pakray
    • 1
  • Alexander Gelbukh
    • 2
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.Centro de Investigación en Computación, Instituto Politécnico NacionalMexico CityMexico

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