Tweet Segmentation for Named Entity Recognition

Abstract

Abstract

Twitter is having lots of users to allocate and distribute a large amount of recent information, various submission in Information Retrieval-IR and Natural Language Processing-NLP undergo harshly through the deafening and tinny kind of tweets. We recommend tweet segmentation framework in a group, called HybridSeg. By dividing tweets with significant segments, the background information is conserved and simply extract with the downstream applications. HybridSeg search the best segmentation of a tweet by increasing the addition of the stickiness score. Two tweet data sets is an experiment; it shows that tweet segmentation quality is extensively increased by learning both global as well as local contexts compared by using global context alone. Additional accuracy is able to name entity recognition by putting segment-based part-of-speech (POS) tagging.

 Keywords: Information Retrieval, Natural language Processing, Named Entity Recognition (NER), Part of Speech (POS)

Cite this Article

Krushnadeo Tanaji Belerao, Tweet Segmentation for Named Entity Recognition. Journal of Artificial Intelligence Research & Advances. 2016; 3(3): 22–25p.