| Título |
Dependency-based semantic parsing for concept-level text analysis |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 15th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2014 |
| Resumen |
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. |
| Observaciones |
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Lugar |
Kathmandu |
| País |
Nepal |
| No. de páginas |
113-127 |
| Vol. / Cap. |
Vol. 8403 LNCS, Issue PART 1 |
| Inicio |
2014-01-01 |
| Fin |
|
| ISBN/ISSN |
|
