Learning Robust Word Representations for Natural Language Processing
| Project/Area Number |
24800041
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
DUH Kevin 奈良先端科学技術大学院大学, 情報科学研究科, 助教 (80637322)
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| Project Period (FY) |
2012-08-31 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2013: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 自然言語処理 / 機械学習 |
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| Research Abstract |
Language is a highly productive phenomenon; new words and expressions are constantly being invented. Current Natural Language Processing techniques have difficulty handling new words, so their performance on real-world text suffers. To address this, we develop robust models of word semantics, focusing on Deep Learning methods for learning vector word representations. Furthermore, we show improvements in parsing and translation performance using systems that incorporate these word representations.
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Report
(3 results)
Research Products
(7 results)
