| Título |
An Open-Domain Cause-Effect Relation Detection from Paired Nominals |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 13th Mexican International Conference on Artificial Intelligence |
| Resumen |
We present a supervised method for detecting causal relations from text. Various kinds of dependency relations, WordNet features, Parts-of-Speech (POS) features along with several combinations of these features help to improve the performance of our system. In our experiments, we used SemEval-2010 Task #8 data sets. This system used 7954 instances for training and 2707 instances for testing from Task #8 datasets. The J48 algorithm was used to identify semantic causal relations in a pair of nominals. Evaluation result gives an overall F1 score of 85.8% of causal instances. |
| Observaciones |
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Lugar |
Tuxtla Gutiérrez |
| País |
Mexico |
| No. de páginas |
263–271 |
| Vol. / Cap. |
8857 |
| Inicio |
2014-11-16 |
| Fin |
2014-11-22 |
| ISBN/ISSN |
978-3-319-13649-3 |
