短句语义向量计算方法

陈福; 林闯; 薛超; 徐月梅; 孟坤; 倪艺函

doi:10.11959/j.issn.1000-436x.2016018

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短句语义向量计算方法

学术论文 | 更新时间：2024-06-05

- 短句语义向量计算方法
- Vector semantic computing method study for short sentence
- 通信学报 2016年37卷第2期页码：11-19
- 作者机构：
  
  1. 北京外国语大学计算机系，北京 100089
  2. 清华大学计算机系，北京 100084
- 作者简介：
  
  [ "陈福（1973），男，辽宁朝阳人，北京外国语大学副教授，主要研究方向为下一代互联网及其管理、跨语言网络空间信息采集与分析、进程代数。" ]
  [ "林闯（1948-），男，辽宁沈阳人，清华大学教授、博士生导师，主要研究方向为计算机网络、系统性能评价、安全分析和随机Petri网。" ]
  [ "薛超（1988），男，陕西渭南人，清华大学博士生，主要研究方向为网络体系结构的性能评价与优化、云计算虚拟资源调度等。" ]
  [ "徐月梅（1985），女，广西梧州人，博士，北京外国语大学讲师，主要研究方向为数据中心网等。" ]
  [ "孟坤（1980），男，河南洛阳人，清华大学助理研究员，主要研究方向为性能评价和随机模型。" ]
  [ "倪艺函（1994），女，江苏连云港人，北京外国语大学博士生，主要研究方向为进程代数。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61170209);国家自然科学基金资助项目(61173008);国家自然科学基金资助项目(61502038);国家自然科学基金资助项目(61370132);教育部新世纪优秀人才支持计划基金资助项目(NCET-13-0676);2011重点课题基金资助项目(BFSU2011-ZS04)
- DOI：10.11959/j.issn.1000-436x.2016018
  中图分类号： TP393
- 网络出版日期：2016-02，
  
  纸质出版日期：2016-02-15
- 稿件说明：
移动端阅览
陈福, 林闯, 薛超, 等. 短句语义向量计算方法[J]. 通信学报, 2016,37(2):11-19.

Fu CHEN, Chuang LIN, Chao XUE, et al. Vector semantic computing method study for short sentence[J]. Journal on communications, 2016, 37(2): 11-19.
陈福, 林闯, 薛超, 等. 短句语义向量计算方法[J]. 通信学报, 2016,37(2):11-19. DOI： 10.11959/j.issn.1000-436x.2016018.

Fu CHEN, Chuang LIN, Chao XUE, et al. Vector semantic computing method study for short sentence[J]. Journal on communications, 2016, 37(2): 11-19. DOI： 10.11959/j.issn.1000-436x.2016018.

摘要

提出了一种基于人工神经网络的短文语义向量放缩算法，结合社交节点自身信息和短文语义，给出社交网络短文语义计算方法和突发话题发现算法。通过文本数值化实现语义距离的计算、比较、节点的分类及社区发现等。通过自行开发的微博采集工具Argus采集的大量新浪微博内容对所提模型和算法进行了验证，最后对未来工作进行了展望。

Abstract

A vector semantic computing method study for short sentence based on artificial neural network was proposed. And a semantic computational algorithm for social network texts as well as a discovery algorithm for mergencies was provided with reference to the information provided by the social nodes itself and the semantic of the text. Through the numerization of text

the calculation and comparison of semantic distance

the classification of nodes and the discovery of community can be realized. Then

huge quantities of Sina Weibo contents are collected to verify the model and algorithm put forward. In the end

outlooks for future jobs are provided.

关键词

Keywords

references

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