文章基本信息

标题：Achieving High Quality Tweet Segmentation using the HybridSeg Framework
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作者：Dr Ilaiah Kavati ; Dayakar P ; E. Amarnath Reddy 等
期刊名称：International Journal of Computer Trends and Technology
电子版ISSN：2231-2803
出版年度：2016
卷号：41
期号：1
页码：37-41
DOI：10.14445/22312803/IJCTT-V41P107
出版社：Seventh Sense Research Group
摘要：Social networking site (Twitter) has attracted several users to share and distribute most modern data, leading to giant volumes of knowledge created every day. In most of the applications, at the time of IR (Information Retrieval) process, data suffers severely from noise and produces the short nature of the tweets. In the present paper, system uses a framework for segmenting the tweets in the form of batch mode, named as HybridSeg. This process easily preserve the semantic data or content by splitting tweets in the form of understandable segments. ‘HybridSeg’ derives the principal segmentation of each and every tweet by maximizing its sum and the stickiness scores of corresponding candidate segments that are to be maintained. HybridSeg is additionally intended to iteratively gain from conﬁdent sections as pseudo criticism. Experiments show that tweet segmentation quality is signiﬁcantly improved.
关键词：HybridSeg; Named Entity Recognition; Twitter; Tweet Segmentation