标题:Modeling Documents with Event Model
作者:Wang Longhui;Zhao Guoguang;Sun Donghong;Li Jun-Bao
作者机构:[Wang, L] Tsinghua University, Beijing, 100000, China, School of Mechanical Engineering, Shandong University, Jinan, 250061, China;[ Zhao, G] Tsinghua 更多
通讯作者:Zhao, Guoguang
来源:Algorithms
出版年:2015
卷:8
期:3
页码:562-572
DOI:10.3390/a8030562
关键词:Event Model;document retrieval;deep learning;neurolinguistics;topic model
摘要:Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944045824&doi=10.3390%2fa8030562&partnerID=40&md5=18d8c7eeac748b9abd8a8f5062349998
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