Name
Abstract | ||
|---|---|---|
The ability to capture the time information conveyed in natural language is essential to many natural language processing applications such as information retrieval, question answering, automatic summarization, targeted marketing, loan repayment forecasting, and understanding economic patterns. In this paper, we propose a graph-based semi-supervised classification strategy that makes use of WordNet definitions or 'glosses', its conceptual-semantic and lexical relations to supplement WordNet entries with information on the temporality of its word senses. Intrinsic evaluation results show that the proposed approach outperforms prior semi-supervised, nongraph classification approaches to the temporality recognition of word senses, and confirm the soundness of the proposed approach. |
Year | Venue | Field |
|---|---|---|
2016 | Frontiers in Artificial Intelligence and Applications | Transduction (machine learning),Temporal orientation,Computer science,Natural language processing,Artificial intelligence,Word sense,Machine learning |
DocType | Volume | ISSN |
Conference | 285 | 0922-6389 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammed Hasanuzzaman | 1 | 0 | 1.35 |
| Gaël Dias | 2 | 354 | 41.95 |
| Stéphane Ferrari | 3 | 5 | 3.16 |