基于特征学习的文本大数据内容理解及其发展趋势

袁书寒; 向阳; 鄂世嘉

doi:10.11959/j.issn.2096-0271.2015030

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基于特征学习的文本大数据内容理解及其发展趋势

专题：网络大数据 | 更新时间：2024-06-03

- 基于特征学习的文本大数据内容理解及其发展趋势
- Text Big Data Content Understanding and Development Trend Based on Feature Learning
- 大数据 2015年1卷第3期页码：65-74
- 作者机构：
  
  同济大学计算机科学与技术系上海 201804
- 作者简介：
  
  [ "袁书寒，男，同济大学博士生，主要研究方向为自然语言处理、深度学习、大数据分析。" ]
  [ "向阳，男，同济大学教授、博士生导师，主要研究方向为大数据分析、云计算、语义计算、管理信息系统，主持和参与多项国家“973”计划、“863”计划、国家科技支撑计划、国家自然科学基金项目，近年来发表论文50余篇。" ]
  [ "鄂世嘉，男，同济大学博士生，CCF学生会员，主要研究方向为云计算、知识图谱、大数据系统。" ]
- 基金信息：
  
  国家重点基础研究发展计划（“973”计划）基金资助项目;The National Basic Research Program of China(973 Program)(2014CB340404);上海市科委科研计划项目;The Science and Technology Planning Project of Shanghai(14511108002)
- DOI：10.11959/j.issn.2096-0271.2015030
  中图分类号： TP391.1
- 网络首发：2015-06，
  
  纸质出版：2015-06-20
- 稿件说明：
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袁书寒, 向阳, 鄂世嘉. 基于特征学习的文本大数据内容理解及其发展趋势[J]. 大数据, 2015,1(3):65-74.

Shuhan Yuan, Yang Xiang, Shijia E. Text Big Data Content Understanding and Development Trend Based on Feature Learning[J]. BIG DATA RESEARCH, 2015, 1(3): 65-74.
袁书寒, 向阳, 鄂世嘉. 基于特征学习的文本大数据内容理解及其发展趋势[J]. 大数据, 2015,1(3):65-74. DOI： 10.11959/j.issn.2096-0271.2015030.

Shuhan Yuan, Yang Xiang, Shijia E. Text Big Data Content Understanding and Development Trend Based on Feature Learning[J]. BIG DATA RESEARCH, 2015, 1(3): 65-74. DOI： 10.11959/j.issn.2096-0271.2015030.

摘要

大数据中蕴含着重要的价值信息，文本大数据作为大数据的重要组成部分，是人类知识的主要载体。特征作为数据内在规律的反映，将文本大数据映射到反映数据本质的特征空间是文本大数据语义理解的重要手段。介绍了文本大数据的特征表示、特征学习，进而梳理了特征学习在文本大数据内容理解中的进展，最后阐述了基于特征学习的文本大数据内容理解未来的发展趋势。

Abstract

Big data contains important value information.Text big data as an important part of big data is the main carrier of human knowledge.Feature represents the inherent law of the data.Mapping the text big data to its feature space which reflects the nature of data is an important method to understand the semantic meaning of the text.Text big data feature representations and feature learning were reviewed.Then the progress of feature learning used in text content understanding was presented.Finally

the future development trends of big text data content understanding were discussed.

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references

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