| Project/Area Number |
16200007
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (A)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
ISHIZUKA Mitsuru The Univ. of Tokyo, Graduate School of Info. Sci. and Tech., Professor, 大学院情報理工学系研究科, 教授 (50114369)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
DOHI Hiroshi The Univ. of Tokyo, Graduate School of Info. Sci. and Tech., Research Associate, 大学院情報理工学系研究科, 助手 (90260504)
OHSAWA Yukio The Univ.of Tokyo, Graduate School of Eng., Associate Professor, 大学院工学系研究科, 助教授 (20273609)
TAKAMA Yasufumi Tokyo Metropolitan Univ., Faculty of System Design, Associate Professor, システムデザイン学部, 助教授 (20313364)
MATSUO Yutaka National Institute of Advanced Industrial Sci. and Tech., Researcher, 研究員 (30358014)
|
|---|
| Project Period (FY) |
2004 – 2006
|
| Project Status |
Completed (Fiscal Year 2006)
|
| Budget Amount *help |
¥49,400,000 (Direct Cost: ¥38,000,000、Indirect Cost: ¥11,400,000)
Fiscal Year 2006: ¥15,860,000 (Direct Cost: ¥12,200,000、Indirect Cost: ¥3,660,000)
Fiscal Year 2005: ¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2004: ¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
|
|---|
| Keywords | Web Intelligence / Artificial Intelligence / Text Mining / Web Mining / Semantic Computing / Text Summarization / Chance Discovery / テキスト処理 / 自動要約 / 人間関係ネットワーク / 意味表現 |
|---|
| Research Abstract |
The World Wide Web (or just Web) has grown to a new global infrastructure for information flow and sharing. In order to enhance Web functions toward creative personals, organization and societies, we have conducted our research on Web intelligence functions especially from the viewpoint of text processing. Below are the main achievements.
1) A new ordering method of extracted important sentences in multi-document summarization: This new method considers not only chronological ordering of the sentences, but also precedence and succession relations in the original texts and topical closeness. This approach possesses high novelty in international level.
2) Relation mining between entities from the Web: Following a human (especially, researcher) relation mining system, we have developed a method and a system for mining relations between firms from the Web. Also, we have undertaken a research and development on a generic framework that mines relations between various entities from the Web.
3)
… More
Textual affect sensing: As one type of text meaning understanding, we have developed two methods that extract affect or emotion from textual expressions. One method has incorporated an advanced function of employing a corpus of commonsense. A classification system of Web online news articles based on their emotion affinity has also been developed.
4) Method for computing semantic similarity between words using a Web search engine: First, we developed a method for computing similarity between words based on the hit counts of the AND search of a Web search engine. Then, in order to improve the performance, we devised an effective method that also employs snippet information of the search engine's output and takes local patterns with the target two words into account.
5) A Common Concept Description Language for natural language texts: As a basis toward next-generation Web, we have carried out our research to set up a common concept description language called CDL.nl. Unlike the Semantic Web which aims to describe meta-data, CDL.nl allows to describe the concept meaning implied in natural language texts in a machine understandable form. This work has been proceeded in parallel with our international standardization activity of CDL.nl. Less
|
