关键词: 微博, 事件追踪, 短文本, Skip-gram模型, 词向量, 语义信息

Abstract: The traditional methods in micro-blog events tracking do not achieve good performance, because the length of micro-blog text is shorter and the cyber-words emerge constantly. To solve this problem, a method of micro-blog event tracking based on word vector is proposed. By using word vector, semantic similarity between the words can be computed, and the accuracy of semantic similarity between micro-blogs can also be improved. Firstly, the Skip-gram model is trained to get the word vector by using a large dataset. Then, the models for initial event and micro-blogs are constructed by extracting the keywords. Finally, the semantic similarities between micro-blogs and the initial event are computed through word vector, and the task of event tracking is completed according to the decision of pre-defined threshold. The experimental results show that the proposed method can make full use of semantic information contained by word vector, which can effectively improve the tracking performance compared with traditional methods.

Key words: micro-blog, event tracking, short text, Skip-gram model, word vector, semantic information