Name
Abstract | ||
|---|---|---|
We propose a method that assists legislation officers in finding inappropriate Japanese legal terms in Japanese statutory sentences and suggests corrections. In particular, we focus on sets of similar legal terms whose usages are defined in legislation drafting rules. Our method predicts suitable legal terms in statutory sentences using Random Forest classifiers, each of which is optimized for each set of similar legal terms. Our experiment shows that our method outperformed existing modern word prediction methods using neural language models. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/978-1-61499-935-5-161 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Japanese Legal Terms,Legal Term Correction,Random Forest | Environmental resource management,Computer science,Knowledge management,Random forest | Conference |
Volume | ISSN | Citations |
313 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takahiro Yamakoshi | 1 | 0 | 1.35 |
| Takahiro Komamizu | 2 | 12 | 10.01 |
| Yasuhiro Ogawa | 3 | 1 | 3.08 |
| Katsuhiko Toyama | 4 | 39 | 11.41 |