短句语义向量计算方法

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

摘要/Abstract

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。

关键词: 在线社会网络, 主题语义计算, 人工神经网络, 突发话题发现

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.

Key words: online social networks, theme semantic computing, artificial neural nets, burst topics discovering

陈福,林闯,薛超,徐月梅,孟坤,倪艺函. 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,Yue-mei XU,Kun MENG,Yi-han NI. Vector semantic computing method study for short sentence[J]. Journal on Communications, 2016, 37(2): 11-19.

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