Name
Abstract | ||
|---|---|---|
Mining Know-Why or explanation knowledge will induce a knowledge of reasoning that is beneficial for our daily use in diagnosis. Then, this framework is for discovering causality existing between causative antecedent and effective consequent discourse units. There are two main problems in the causality extraction; cause-effect identification and cause-effect boundary determination. The cause-effect identification problem can be solved by learning verb pairs and lexico syntactic pattern (NP1 V NP2) from annotated corpus, using the Naïve Bayes classifier. The cause-effect boundary determination problem can be solved by using centering theory and interesting cue phrase or causality link, where the interesting cue phrase would include the discourse markers and verb phrases. Our model of causality extraction shows the precision and recall of 86% and 70% respectively, where our evaluation is based on the expert's results. |
Year | Venue | Keywords |
|---|---|---|
2006 | ACST | explanation knowledge,cause-effect identification,causality extraction,interesting cue phrase,textual data,mining explanation knowledge,discourse marker,causality link,effective consequent discourse unit,cause-effect identification problem,cause-effect boundary determination,cause-effect boundary determination problem |
Field | DocType | ISBN |
Verb,Causality,Naive Bayes classifier,Precision and recall,Phrase,Lexico,Artificial intelligence,Natural language processing,Syntax,Mathematics,Discourse marker | Conference | 0-88986-545-0 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| chaveevan pechsiri | 1 | 19 | 5.58 |
| asanee kawtrakul | 2 | 161 | 25.90 |
| Rapepun Piriyakul | 3 | 5 | 2.18 |