Name
Abstract | ||
|---|---|---|
What would happen if temperatures were subdued and result in a cool summer? One can easily imagine that air conditioner, ice cream or beer sales would be suppressed as a result of this. Less obvious is that agricultural shipments might be delayed, or that sound proofing material sales might decrease. The ability to extract such causal knowledge is important, but it is also important to distinguish between cause-effect pairs that are known and those that are likely to be unknown, or rare. Therefore, in this paper, we propose a method for extracting rare causal knowledge from Japanese financial statement summaries produced by companies. Our method consists of three steps. First, it extracts sentences that include causal knowledge from the summaries using a machine learning method based on an extended language ontology. Second, it obtains causal knowledge from the extracted sentences using syntactic patterns. Finally, it extracts the rarest causal knowledge from the knowledge it has obtained. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/SSCI.2017.8285265 | 2017 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | Field | DocType |
Text Mining,Natural Language Processing,Causal Knowledge | Ontology,Financial statement,Computer science,Natural language processing,Artificial intelligence,Syntax | Conference |
ISBN | Citations | PageRank |
978-1-5386-2727-3 | 1 | 0.43 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Sakaji | 1 | 30 | 17.97 |
| Risa Murono | 2 | 1 | 0.43 |
| Hiroyuki Sakai | 3 | 17 | 4.75 |
| Jason Bennett | 4 | 1 | 0.43 |
| Kiyoshi Izumi | 5 | 127 | 37.12 |