Dependency-based semantic parsing for concept-level text analysis

Citation

Poria S, Agarwal B, Gelbukh A, Hussain A & Howard N (2014) Dependency-based semantic parsing for concept-level text analysis. In: Gelbukh A (ed.) Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I. Lecture Notes in Computer Science, 8403. 15th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014, Kathmandu, Nepal, 06.04.2014-12.04.2014. Berlin Heidelberg: Springer, pp. 113-127. https://doi.org/10.1007/978-3-642-54906-9_10

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.