Abstract
The main task we address in our research is classification of text using fine-grained attitude labels. The developed@AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the fine grained level (14 labels) was 62%, on the middle level (7 labels) - 71%, and on the top level (3 labels) - 88%.
| Original language | English |
|---|---|
| Title of host publication | Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference |
| Pages | 806-814 |
| Number of pages | 9 |
| Volume | 2 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 23rd International Conference on Computational Linguistics, Coling 2010 - Beijing Duration: 2010 Aug 23 → 2010 Aug 27 |
Other
| Other | 23rd International Conference on Computational Linguistics, Coling 2010 |
|---|---|
| City | Beijing |
| Period | 10/8/23 → 10/8/27 |
ASJC Scopus subject areas
- Language and Linguistics
- Computational Theory and Mathematics
- Linguistics and Language