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学习词汇及其参考语的协同效应

Synergies in learning words and their referents
课程网址: http://videolectures.net/nips2010_johnson_slw/  
主讲教师: Mark Johnson
开课单位: 麦格理大学
开课时间: 2011-01-12
课程语种: 英语
中文简介:
本文提出了贝叶斯非参数模型,它们同时学习从音素串中分词并学习其中一些词的指称,并表明在获取这两种语言信息时存在协同相互作用。模型本身是新型的适配器语法,是将主题模型嵌入到PCFG中的扩展。这些模型同时将音素序列分割为单词,并学习非语言对象与引用它们的单词之间的关系。我们展示(i)建模词间依赖性不仅提高了分词的准确性,而且提高了词对象关系,以及(ii)同时学习词对象关系和分词片段的模型比刚学习的词更准确单词分词本身。我们认为这些结果支持语言习得的互动观点,可以利用这些协同作用。
课程简介: This paper presents Bayesian non-parametric models that simultaneously learn to segment words from phoneme strings and learn the referents of some of those words, and shows that there is a synergistic interaction in the acquisition of these two kinds of linguistic information. The models themselves are novel kinds of Adaptor Grammars that are an extension of an embedding of topic models into PCFGs. These models simultaneously segment phoneme sequences into words and learn the relationship between non-linguistic objects to the words that refer to them. We show (i) that modelling inter-word dependencies not only improves the accuracy of the word segmentation but also of word-object relationships, and (ii) that a model that simultaneously learns word-object relationships and word segmentation segments more accurately than one that just learns word segmentation on its own. We argue that these results support an interactive view of language acquisition that can take advantage of synergies such as these.
关 键 词: 贝叶斯非参数模型; 音素串; 适配器语法
课程来源: 视频讲座网
最后编审: 2019-07-25:cwx
阅读次数: 36