Advances in statistical natural language processing using stochastic processes
| Project/Area Number |
24700152
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
MOCHIHASHI Daichi 統計数理研究所, 大学共同利用機関等の部局等, 准教授 (80418508)
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| Research Collaborator |
OHISHI Yasunori NTTコミュニケーション科学基礎研究所
YOSHII Kazuyoshi 産業技術総合研究所/京都大学
UCHIUMI Kei
TSUKAHARA Hiroshi デンソーITラボラトリ
NOJI Hiroshi NII/総合研究大学院大情報学専攻
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 統計的自然言語処理 / ノンパラメトリックベイズ / 統計的機械学習 / ベイズ統計学 / 自然言語処理 / ノンパラメトリックベイズ法 / ガウス過程 / ディリクレ過程 / ロボティクス / 音楽・音響処理 / 確率過程 |
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| Outline of Final Research Achievements |
While unsupervised speech recognition was superseded by Glass et al. (ACL 2012) as a statistical natural language processing research leveraging stochastic processes, we could show new models in music information processing and signal processing in this area. Specifically, we proposed a novel non-negative matrix factorization for music information processing utilizing periodic frequencies with Gaussian processes, and an unsupervised multi-labeling infinite HMM using Markov Indian buffet processes.
In a statistical natural processing area, we could propose a novel language nonparametric Bayesian n-gram language models adapting to topical contexts, and an unsupervised morphological analysis with latent word classes.
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Report
(4 results)
Research Products
(16 results)
