图 书 馆
词汇及其指称词学习中的协同作用Synergies in learning words and their referents |
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| 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: | 2011-03-25 |
| 课程语种: | 英语 |
| 中文简介: |  本文介绍了贝叶斯非参数模型，该模型同时学习从音素字符串中分割单词并学习其中一些单词的指代对象，并表明在这两种语言信息的获取中存在协同作用。这些模型本身是新颖的适配器语法，是将主题模型嵌入PCFG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。我们证明（i）建立词间依存关系模型不仅可以提高分词的准确性，而且可以提高词对象关系的准确性，并且（ii）与同时学习词对象关系和分词片段的模型相比，该模型可以更准确地学习词对象关系和分词片段分词本身。我们认为，这些结果支持可以利用诸如此类协同作用的语言习得的交互式视图。 |
| 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; 单词分割 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-07：zyk |
| 最后编审: | 2021-03-10：zyk |
| 阅读次数: | 18 |