PPT-Semi-Supervised Recognition of Sarcastic Sentences

in Twitter and Amazon Smit Shilu Problem Semisupervised identification of sarcasm in datasets from popular sites such as Twitter and Amazon What is Sarcasm T he activity of saying or writing the opposite of what you mean or of speaking in a way intended to make someone else feel stupid or show them that you are . using . Attributes and Comparative Attributes. Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta. The Robotics Institute. Carnegie Mellon University. Supervision. Supervised. Active. Learning. Big-Data. : . Using . an author’s historical tweets to predict sarcasm . Anupam. Khattri. 2. , Aditya Joshi. 1,3. , . Pushpak Bhattacharyya. 1. , Mark James Carman. 3. 1 . Indian Institute of Technology Bombay, India. Introductions . Name. Department/Program. If research, what are you working on.. Your favorite fruit.. How do you estimate P(. y|x. ) . Types of Learning. Supervised Learning. Unsupervised Learning. Semi-supervised Learning. To say something and mean the opposite. Looks really warm out there today. Fun Fact. Sarcasm or irony is used once every 2 minutes in conversation!!. http://safeshare.tv/w/VvqFlxXUfn. (What is Verbal Irony/ Sarcasm?). Yacine . Jernite. Text-as-Data series. September 17. 2015. What do we want from text?. Extract information. Link to other knowledge sources. Use knowledge (Wikipedia, . UpToDate,…). How do we answer those questions?. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Dena B. French, . EdD. , RDN, . LD. ISPP Program Director & Experiential Coordinator. ISPP Class of 2017. Objectives. What is an ISPP?. Fontbonne’s. ISPP. Campus . “Tour”. Program overview & curriculum . Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. 12019According to Family Code Section 3200 all providers of supervised visitation mustoperate their programs in compliance with the Uniform Standards of Practice for Providers of Supervised Visitation Dongyeop. Kang. 1. , Youngja Park. 2. , Suresh . Chari. 2. . 1. . . IT Convergence Laboratory, KAIST . Institute,Korea. 2. . IBM T.J. Watson Research . Center, NY, USA. Algorithms and Applications. Christoph F. . Eick. Department of Computer Science. University of Houston. Organization of the Talk. Motivation—why is it worthwhile generalizing machine learning techniques which are typically unsupervised to consider background information in form of class labels? . Unsu. pervised . approaches . for . word sense disambiguation. Under the guidance of. Slides by. Arindam. . Chatterjee. &. Salil. Joshi. Prof. . Pushpak . Bhattacharyya. May 01, 2010. roadmap. Bird’s Eye View.. Aditya . Joshi. IITB-. Monash. Research Academy. Joint work with . Abhijit. . Mishra. , Vinita Sharma, . Balamurali. AR,. . Prof. Pushpak Bhattacharyya, Prof. Mark James Carman. 1. Contact email: adityaj@cse.iitb.ac.in . with Incomplete Class Hierarchies. Bhavana Dalvi. , Aditya Mishra, William W. Cohen. Semi-supervised Entity Classification. 2. Semi-supervised Entity Classification. Subset. 3. Disjoint. Semi-supervised Entity Classification.