| Presentation | 2006-02-03 A Study toward Automatic Illustration of Legal Text Takao Ishizuka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a new method for automatic illustration of legal text. The reasons which make us difficult to understand most of laws are chiefly based on the macroscopic structure of the law system and the microscopic syntax of each article of the law. Our method quantizes information content by the ratio of the number of old words to the number of new words in an article of the law. Old words mean words emerging from the article 1 to the predecessor article of the law. We can illustrate a tree diagram by the distribution of the information content semi-automatically. Further, we develop a program of clustering predicate pattern used in the sentence of the whole articles of the law. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Automatic Illustration / Legal Text / Information Content of the Article |
| Paper # | NLC2005-122 |
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| Conference Information | |
| Committee | NLC |
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| Conference Date | 2006/1/27(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
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| Vice Chair | |
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| Paper Information | |
| Registration To | Natural Language Understanding and Models of Communication (NLC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study toward Automatic Illustration of Legal Text |
| Sub Title (in English) | |
| Keyword(1) | Automatic Illustration |
| Keyword(2) | Legal Text |
| Keyword(3) | Information Content of the Article |
| 1st Author's Name | Takao Ishizuka |
| 1st Author's Affiliation | Faculty of Business Administration, Asia University() |
| Date | 2006-02-03 |
| Paper # | NLC2005-122 |
| Volume (vol) | vol.105 |
| Number (no) | 595 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |