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http://hdl.handle.net/1893/20590| Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
| Peer Review Status: | Refereed |
| Author(s): | Poria, Soujanya Agarwal, Basant Gelbukh, Alexander Hussain, Amir Howard, Newton |
| Contact Email: | amir.hussain@stir.ac.uk |
| Title: | Dependency-based semantic parsing for concept-level text analysis |
| Editor(s): | Gelbukh, A |
| 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 |
| Issue Date: | 2014 |
| Date Deposited: | 9-Jul-2014 |
| Series/Report no.: | Lecture Notes in Computer Science, 8403 |
| Conference Name: | 15th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014 |
| Conference Dates: | 2014-04-06 - 2014-04-12 |
| 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: | VoR - Version of Record |
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| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
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| Dependency-based semantic parsing 2014.pdf | Fulltext - Published Version | 206.48 kB | Adobe PDF | Under Embargo until 3000-03-31 Request a copy |
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