Dependency-Based Semantic Parsing for Concept-Level Text Analysis

  • Soujanya Poria
  • Basant Agarwal
  • Alexander Gelbukh
  • Amir Hussain
  • Newton Howard
Conference paper

DOI: 10.1007/978-3-642-54906-9_10

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8403)
Cite this paper as:
Poria S., Agarwal B., Gelbukh A., Hussain A., Howard N. (2014) Dependency-Based Semantic Parsing for Concept-Level Text Analysis. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403. Springer, Berlin, Heidelberg

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Soujanya Poria
    • 1
    • 4
    • 6
  • Basant Agarwal
    • 2
  • Alexander Gelbukh
    • 3
  • Amir Hussain
    • 4
  • Newton Howard
    • 5
  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

Personalised recommendations