|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||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, Berlin Heidelberg: Springer. 15th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014, 6.4.2014 - 12.4.2014, Kathmandu, Nepal, pp. 113-127.|
|Series/Report no.:||Lecture Notes in Computer Science, 8403|
|Conference Name:||15th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014|
|Conference Location:||Kathmandu, Nepal|
|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.|
|Status:||Book Chapter: publisher version|
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