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)


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.


Causal Relation Noun Phrase Semantic Relation Natural Language Text Receiver Operating Characteristic Space 
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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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