Presentation | 2000/11/27 Acquiring word meanings in a small world using neural networks Mats Nystrand, Kazuhiro Ueda, Naoto Takahashi, |
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Abstract(in English) | This paper presents the results from training a neural network on word understanding in short questions about the state of a simulated small world. The neural network receives its input from two sources: 1)a question list and 2)a data structure of the simulated objects and their locations. The output of the network is represented as one or more words from a fixed word list. Back propagation has been used as learning algorithm and the results from several learning tasks are presented. This paper also shows that for a limited range of input a plain non-recurrent three-layer network can learn to answer correctly to questions regarding the position of objects in the small world. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Question & Answering Systems / Neural Networks / Natural Language Processing / Knowledge Acquisition |
Paper # | TL2000-30 |
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Committee | TL |
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Conference Date | 2000/11/27(1days) |
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Registration To | Thought and Language (TL) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Acquiring word meanings in a small world using neural networks |
Sub Title (in English) | |
Keyword(1) | Question & Answering Systems |
Keyword(2) | Neural Networks |
Keyword(3) | Natural Language Processing |
Keyword(4) | Knowledge Acquisition |
1st Author's Name | Mats Nystrand |
1st Author's Affiliation | University of Tokyo, Graduate School of International and Interdisciplinary() |
2nd Author's Name | Kazuhiro Ueda |
2nd Author's Affiliation | University of Tokyo, Graduate School of International and Interdisciplinary |
3rd Author's Name | Naoto Takahashi |
3rd Author's Affiliation | University of Tokyo, Graduate School of International and Interdisciplinary |
Date | 2000/11/27 |
Paper # | TL2000-30 |
Volume (vol) | vol.100 |
Number (no) | 480 |
Page | pp.pp.- |
#Pages | 8 |
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