Tweet Segmentation for Named Entity Recognition
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.
Chenliang Li, Aixin Sun, Jianshu Weng, and Qi He,sMember, IEEE," Tweet Segmentation and its Application to Named Entity Recognition", IEEE Transactions on knowledge and data engineering Vol. 27,No.2, February 2015.
Chenliang Li, Jianshu Weng, Qi He, Yuxia Yao, Anwitaman Datta, Aixin Sun, and Bu-
Sung Lee" TwiNER: Named Entity Recognition in Targeted Twitter Stream", SIGIRâ€.12,
August 12-16, 2012, Portland, Oregon, USA.
Chung-Hong Lee, " Unsupervised and supervised learning to evaluate event relatedness based on content mining from social-media streams", Year-2012 Elsevier.
Ji Aoa, Peng Zhanga, Yanan Caoa," Estimating the Locations of Emergency Events from
Twitter Streams", Elsevier 2014 [2nd International Conference on Information Technology
and Quantitative Management, ITQM 2014]
Zaiqing Nie, Ji-Rong Wen, and Wei-Ying Ma, Fellow," Statistical Entity Extraction from
Web", IEEE Vol.100 No.9 Year 2012.
Zhen Liao, Yang Song, Yalou Huang, Li-wei He, Qi He," Task Trail: An E_ective Segmentation of User Search Behavior", IEEE Vol:PP NO:99 Year 2014
Deniz Karatay, and Pinar Karagoz ," User Interest Modeling in Twitter with Named Entity Recognition",Microposts 2015
Alan Ritter, Sam Clark, Mausam and Oren Etzioni,"Named Entity Recognition in Tweets:An Experimental Study",2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK, July 27-31, 2011
Xiuzhen Zhang, Lishan Cui, and Yan Wang, Senior Member, IEEE "Computing Multi-
Dimensional Trust by Mining E-Commerce Feedback Comments" IEEE Transactions on
knowledge and data engineering Vol:26 No:7 Year 2014
Ikuya Yamada, Hideaki Takeda, Yoshiyasu Takefuji, "Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking" ACL 2015 Workshop on Noisy User-generated Text, pages 136-140,Beijing, China, July 31, 2015.
- There are currently no refbacks.