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[22] Graham Neubig - Unsupervised Learning of Lexical Information

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Manage episode 302418423 series 2982803
内容由The Thesis Review and Sean Welleck提供。所有播客内容(包括剧集、图形和播客描述)均由 The Thesis Review and Sean Welleck 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
Graham Neubig is an Associate Professor at Carnegie Mellon University. His research focuses on language and its role in human communication, with the goal of breaking down barriers in human-human or human-machine communication through the development of NLP technologies. Graham’s PhD thesis is titled "Unsupervised Learning of Lexical Information for Language Processing Systems", which he completed in 2012 at Kyoto University. We discuss his PhD work related to the fundamental processing units that NLP systems use to process text, including non-parametric Bayesian models, segmentation, and alignment problems, and discuss how his perspective on machine translation has evolved over time. Episode notes: http://cs.nyu.edu/~welleck/episode22.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at http://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
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Manage episode 302418423 series 2982803
内容由The Thesis Review and Sean Welleck提供。所有播客内容(包括剧集、图形和播客描述)均由 The Thesis Review and Sean Welleck 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal
Graham Neubig is an Associate Professor at Carnegie Mellon University. His research focuses on language and its role in human communication, with the goal of breaking down barriers in human-human or human-machine communication through the development of NLP technologies. Graham’s PhD thesis is titled "Unsupervised Learning of Lexical Information for Language Processing Systems", which he completed in 2012 at Kyoto University. We discuss his PhD work related to the fundamental processing units that NLP systems use to process text, including non-parametric Bayesian models, segmentation, and alignment problems, and discuss how his perspective on machine translation has evolved over time. Episode notes: http://cs.nyu.edu/~welleck/episode22.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at http://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.patreon.com/thesisreview or www.buymeacoffee.com/thesisreview
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