短文本信息流的无监督会话抽取技术

文本会话抽取将网络聊天记录等短文本信息流中的信息根据其所属的会话分检到多个会话队列,有利于短文本信息的管理及进一步的挖掘.现有的会话抽取技术主要对基于文本相似度的聚类方法进行改进,面临着短文本信息流的特征稀疏性、奇异性和动态性等挑战.针对这些挑战,研究无监督的会话抽取技术,提出了一种基于信息流时序特征和上下文相关度的抽取方法.首先研究了信息流的会话生命周期规律,提出基于信息产生频率的会话边界检测方法；其次提出信息间的上下文相关度概念,采用基于实例的机器学习方法计算该相关度；最后综合信息产生频率和上下文相关度,设计了基于Single-Pass聚类模型的会话在线抽取算法SPFC(single-pass based on frequency and correlation).真实数据集上的实验结果表明,SPFC算法与已有的基于文本相似度的会话抽取算法相比,F1评测指标提高了30％.

Unsupervised Conversation Extraction in Short Text Message Streams

Short text message streams are produced by Short Message Service,Instant Messager and BBS,which are widely used.Each stream usually contains.Extracting the conversations in the streams is helpful to various applications including business intelligence,investigation of crime and public opinion analysis.Existing research mainly based on text similarity encounter challenges such as the anomaly,dynamics,and the sparse eigenvector of short text message.This paper proposes an innovative conversation extraction method to cover the challenges.Firstly,the study detects the conversation boundary of short text message streams using temporal feature;secondly,contextually correlative degree is introduced to replace similar degree,and an instance-based machine learning method is proposed to compute the correlative degree.Finally,the study designs Single-Pass based conversation extraction algorithm SPFC(single-pass based on frequency and correlation),which combines the temporal and contextually correlative characteristics.Experimental results on a large real Chinese dataset show that this method SPFC improves the performance by 30% when compared with the best existing variation algorithm in terms of F1 measure.