图 书 馆
自然语言处理的无监督学习Unsupervised Learning for Natural Language Processing |
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| 课程网址: | http://videolectures.net/uai08_klein_ul/ |
| 主讲教师: | Dan Klein |
| 开课单位: | 加州大学 |
| 开课时间: | 2008-07-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Given the abundance of text data, unsupervised approaches are very appealing for natural language processing. We present three latent variable systems which achieve state-of-the-art results in domains previously dominated by fully supervised systems. For syntactic parsing, we describe a grammar induction technique which begins with coarse syntactic structures and iteratively refines them in an unsupervised fashion. The resulting coarse-to-fine grammars admit efficient coarse-to-fine inference schemes and have produced the best parsing results in a variety of languages. For co reference resolution, we describe a discourse model in which entities are shared across documents using a hierarchical Dirichlet process. In each document, entities are repeatedly rendered into mention strings by a sequential model of attentional state and anaphoric constraint. Despite being fully unsupervised, this approach is competitive with the best supervised approaches. Finally, for machine translation, we present a model which learns translation lexicons from non-parallel corpora. Alignments between word types are modeled by a prior over matchings. Given any fixed alignment, a joint density over word vectors derives from probabilistic canonical correlation analysis. This approach is capable of discovering high-precision translations, even when the underlying corpora and languages are divergent. |
| 关 键 词: | 自然语言处理; 感应技术; 匹配模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-27：zyk |
| 阅读次数: | 61 |