Name
Abstract | ||
|---|---|---|
Various supervised algorithms for mining causal relations from large corpora exist. These algorithms have focused on relations explicitly expressed with causal verbs, e.g. "to cause". However, the challenges of extracting causal relations from domain-specific texts have been overlooked. Domain-specific texts are rife with causal relations that are implicitly expressed using verbal and non-verbal patterns, e.g. "reduce", "drop in", "due to". Also, readily-available resources to support supervised algorithms are inexistent in most domains. To address these challenges, we present a novel approach for causal relation extraction. Our approach is minimally-supervised, alleviating the need for annotated data. Also, it identifies both explicit and implicit causal relations. Evaluation results revealed that our technique achieves state-of-the-art performance in extracting causal relations from domain-specific, sparse texts. The results also indicate that many of the domain-specific relations were unclassifiable in existing taxonomies of causality. |
Year | Venue | Keywords |
|---|---|---|
2011 | NLDB | novel approach,domain-specific text,supervised algorithm,causal relation extraction,causal relation,various supervised algorithm,domain-specific relation,implicit causal relation,mining causal relation,causal verb,information extraction |
Field | DocType | Volume |
Causality,Causal relations,Computer science,Information extraction,Natural language processing,Artificial intelligence | Conference | 6716 |
ISSN | Citations | PageRank |
0302-9743 | 10 | 0.64 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashwin Ittoo | 1 | 61 | 6.58 |
| Gosse Bouma | 2 | 483 | 70.88 |