Dependency-based semantic parsing for concept-level text analysis

Poria, Soujanya; Agarwal, Basant; Gelbukh, Alexander; Hussain, Amir; Howard, Newton

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.

Citation

Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., & Howard, N. (2014, April). Dependency-based semantic parsing for concept-level text analysis. Presented at 15th International Conference, CICLing 2014, Kathmandu, Nepal

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 15th International Conference, CICLing 2014
• Start Date: Apr 6, 2014
• End Date: Apr 12, 2014
• Publication Date: 2014
• Deposit Date: Sep 26, 2019
• Publisher: Springer
• Pages: 113-127
• Series Title: Lecture Notes in Computer Science
• Series Number: 8403
• Series ISSN: 1611-3349
• Book Title: Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I
• ISBN: 9783642549052
• DOI: https://doi.org/10.1007/978-3-642-54906-9_10
• Keywords: Emotion Recognition; Sentiment Analysis; Dependency Relation; Birthday Party; Natural Language Text
• Public URL: http://researchrepository.napier.ac.uk/Output/1793037