Dependency-Based Semantic Parsing for Concept-Level Text Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8403)


Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks. Concept extraction from text is a key step in concept-level text analysis. In this paper, we propose a ConceptNet-based semantic parser that deconstructs natural language text into concepts based on the dependency relation between clauses. Our approach is domain-independent and is able to extract concepts from heterogeneous text. Through this parsing technique, 92.21% accuracy was obtained on a dataset of 3,204 concepts. We also show experimental results on three different text analysis tasks, on which the proposed framework outperformed state-of-the-art parsing techniques.


Emotion Recognition Sentiment Analysis Dependency Relation Birthday Party Natural Language Text 
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-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Electrical & Electronic EngineeringNanyang Technological UniversitySingapore
  2. 2.Department of Computer EngineeringMalaviya National Institute of TechnologyIndia
  3. 3.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico
  4. 4.Department of Computing Science and MathematicsUniversity of StirlingUK
  5. 5.MIT Media LaberotoryMITUSA
  6. 6.The Brain Sciences FoundationCambridgeUSA

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