| Project/Area Number |
09680356
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 Electro-Communications |
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Principal Investigator |
OZEKI Kazuhiko The University of Electro-Communications, Faculty of Electro-Communications, Research Associate, 電気通信学部, 教授 (50214135)
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| Co-Investigator(Kenkyū-buntansha) |
張 玉潔 電気通信大学, 電気通信学部, 助手
高木 一幸 電気通信大学, 電気通信学部, 助手 (70272755)
ZHANG Yujie The University of Electro-Communications, Faculty of Electro-Communications, Research Associate
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,000,000 (Direct Cost: ¥3,000,000)
Fiscal Year 1999: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1998: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1997: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | dependency analysis / minimum total penalty method / minimum cost segmentation problem / bunsetsu segmentation / segmentation of long sentence / decision tree / prosodic information / sentence compaction / 係り受け規則 / 文要約 / 字幕生成 / 確率的CYKアルゴリズム |
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| Research Abstract |
Results of this research project can be classified into 1. theoretical basis, 2. bunsetsu segmentation and segmentation of long sentences, 3. use of prosodic information, and 4. sentence compaction, all related to the minimum total penalty method.
1. Dependency analysis was investigated from a view point of "minimum cost segmentation problem". It was shown that various dependency analysis algorithms can be derived by changing the definition of the cost. It was also made clear that the minimum total penalty method allows the use of a wide range of numerical information as linguistic knowledge.
2. It is necessary to segment a sentence into bunsetsu phrases prior to dependency analysis. In this work, a decision tree method was applied to this problem, giving higher segmentation accuracy than conventional methods data. The decision tree technique was also applied to segmentation of long sentences, which is pre-processing for dependency analysis. It was demonstrated that a set of segmentation
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rules was automatically acquired by this method.
3. To find out syntactic information contained in prosodic features, a statistical model was created that represents a relationship between prosodic features and inter-phrase dependency distance. The model was then incorporated into the minimum total penalty parser to measure the effectiveness of prosodic information for dependency analysis. The duration of pause was found to be very effective. Further investigation is necessary to make use of prosodic features related to pitch, power, and speaking rate.
4. An efficient sentence compaction algorithm was developed for such application as generation of on-line TV closed-captions. This algorithm selects an optimal bunsetsu subsequence from an original sentence that maximizes the sum of bunsetsu importance scores and inter-phrase dependency scores. Future work includes investigation of better definitions of bunsetsu importance score and inter-phrase dependency score. It is also necessary to evaluate the quality of shortened sentences using large amount of test data. Less
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