2004 Fiscal Year Final Research Report Summary
Development of Question Answering Techologies on Large-Scale Text
| Project/Area Number |
14380155
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
KATO Tsuneaki The University of Tokyo, Graduate School of Arts and Science, Associate Professor, 大学院・総合文化研究科, 助教授 (60334299)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAZAWA Tsuneko The University of Tokyo, Graduate School of Arts and Science, Associate Professor, 大学院・総合文化研究科, 助教授 (00292839)
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| Project Period (FY) |
2002 – 2004
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| Keywords | Question Answering / Dialogue Processing / Text Processing / Information Access / Information Retrieval / Information Extraction |
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| Research Abstract |
Question Answering (QA) on large-scale text is technology for answering any question from users by referring to stored large-scale text. It allows users to obtain the necessary information itself with consideration of the interest and perspective expressed in their questions. Our research aims at to develop QA and to establish it as a sophisticated user-centered information access means, and achieved the following results.
First, QA technologies for answering series of related questions interactively were examined, which would replace the current QA technologies for isolated questions. Assuming that such a technology helps report writing, we examined what kind of questions are made there and what procedures are needed for answering those questions. Based on the findings of the examination, we designed a novel task for measuring the abilities useful for interactive QA has been and constructed a test set of that task. Analysis of the state-of-art technologies by this task revealed that we have still have a large room to handling those situations, and some theoretical problems remain in handling list-type questions, which request systems to enumerate all and only correct answers.
Second, incorporating a visual mode such as usage of charts into the current text-based QA was examined. In this part of the research, we showed that an appropriate use of charts get possible by considering users' intention, and proposed a method for eliciting such an intention thorough dialogues. We pushed this multi-modal QA forward and proposed a novel research topic, "summarization and visualization on trend information", that covers the fields of text summarization, information visualization and QA. Presenting a framework of research, we constructed a data set for the research, which includes a corpus of semantically annotated newspaper articles of an originally designed coding system useful to summarization and visualization.
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