Large-scale and massively parallel computing for global optimization in natural language processing
Project/Area Number |
23700177
|
Research Category |
Grant-in-Aid for Young Scientists (B)
|
Allocation Type | Multi-year Fund |
Research Field |
Intelligent informatics
|
Research Institution | Nara Institute of Science and Technology |
Principal Investigator |
KOMACHI Mamoru 奈良先端科学技術大学院大学, 情報科学研究科, 助教 (60581329)
|
Project Period (FY) |
2011 – 2012
|
Project Status |
Completed (Fiscal Year 2012)
|
Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2011: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
Keywords | 自然言語処理 / ビッグデータ / 半教師あり学習 / 知識獲得 / 情報抽出 / 単語クラスタリング / クエリマイニング / オークション |
Research Abstract |
In recent years, semi-supervised learning has been receiving more and more attentions in natural language processing. Semi-supervised learning uses large-scale unlabeled data in addition to small number of labeled data (seed) to perform highly accurate statistical learning. This research project showed that the structure of graph affects the performance of semi-supervised methods in several tasks.
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Report
(3 results)
Research Products
(11 results)