面向机器学习的程序设计语言的语法和编译器Towards Machine Learning of Grammars and Compilers of Programming Languages |
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课程网址: | http://videolectures.net/ecmlpkdd08_nakamura_tmlal/ |
主讲教师: | Keita Imada; Katsuhiko Nakamura |
开课单位: | 东京电机大学 |
开课时间: | 2008-10-10 |
课程语种: | 英语 |
中文简介: | 关于政治和社会问题的两极分化讨论在大众和用户生成的媒体中很常见。然而,基于计算机的思想话语理解被认为太难进行。本文提出了意识形态话语的统计模型。我们所说的意识形态是“一群人所共有的一套普遍信仰”,例如,民主党和共和党是美国两大政治意识形态。该模型捕获了由于意识形态文本的主题和作者或演讲者的意识形态观点而产生的词汇变化。为了解决逻辑正态先验的非共轭性问题,我们推导了该模型的变分推理算法。我们评估了综合数据模型以及书面和口头的政治话语。实验结果强烈支持意识形态观点在词汇变异中的反映。 |
课程简介: | Polarizing discussions on political and social issues are common in mass and user-generated media. However, computer-based understanding of ideological discourse has been considered too difficult to undertake. In this paper we propose a statistical model for ideology discourse. By ideology we mean ``a set of general beliefs socially shared by a group of people.'' For example, Democratic and Republican are two major political ideologies in the United States. The proposed model captures lexical variations due to an ideological text's topic and due to an author or speaker's ideological perspective. To cope with the non-conjugacy of the logistic-normal prior we derived a variational inference algorithm for the model. We evaluate the proposed model on synthetic data as well as a written and a spoken political discourse. Experimental results strongly support that ideological perspectives are reflected in lexical variations. |
关 键 词: | 计算机科学; 机器学习; 数据 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-29:heyf |
阅读次数: | 20 |