图 书 馆
线性模型是正确的语言吗？Are Linear Models Right for Language? |
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| 课程网址: | http://videolectures.net/clsp_pereira_linear/ |
| 主讲教师: | Fernando C. N. Pereira |
| 开课单位: | 谷歌公司 |
| 开课时间: | 2012-02-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Over the last decade, linear models have become the standard machine learning approach for supervised classification, ranking, and structured prediction natural language processing. They can handle very high-dimensional problem representations, they are easy to set up and use, and they extend naturally to complex structured problems. But there is something unsatisfying in this work. The geometric intuitions behind linear models were developed with low-dimensional, continuous problems, while natural language problems involve very high dimension, discrete representations with long tailed distributions. Do the orignal intuitions carry over? In particular, do standard regularization methods make any sense for language problems? I will give recent experimental evidence that there is much to do in making linear model learning more suited to the statistics of language. |
| 关 键 词: | 计算机科学; 计算语言学; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-15：wuyq |
| 阅读次数: | 43 |