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 . hujiacil Dmitry Davidov ICNC The Hebrew University Jerusalem Israel dmitryalicenchujiacil Ari Rappoport Institute of Computer Science The Hebrew University Jerusalem Israel wwwcshujiacil arir Abstract Sarcasm is a sophisticated form of speech act wid using . Attributes and Comparative Attributes. Abhinav Shrivastava, Saurabh Singh, Abhinav Gupta. The Robotics Institute. Carnegie Mellon University. Supervision. Supervised. Active. Learning. Big-Data. William Cohen. 1. Review – . Graph Algorithms so far….. PageRank and how to scale it up. Personalized PageRank/Random Walk with Restart and. how to implement it. how to use it for extracting part of a graph. Low-Resource Languages. Dan . Garrette. , Jason . Mielens. , and Jason . Baldridge. Proceedings of ACL 2013. Semi-Supervised Training. HMM with Expectation-Maximization (EM). Need:. Large . raw. corpus. John Blitzer. 自然语言计算组. http://research.microsoft.com/asia/group/nlc/. Why should I know about machine learning? . This is an NLP summer school. Why should I care about machine learning?. 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. 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?. Several slides from . Luke . Xettlemoyer. , . Carlos . Guestrin. and Ben . Taskar. Typical Paradigms of Recognition. Feature Computation. Model. Visual Recognition. Identification. Is this your car?. Classification. with Incomplete Class . Hierarchies. Bhavana Dalvi. ¶. *. , Aditya Mishra. †. , and William W. Cohen. *. ¶ . Allen Institute . for . Artificial Intelligence, . * . School Of Computer Science. Introduction. Labelled data. Unlabeled data. cat. dog. (Image of cats and dogs without labeling). Introduction. Supervised learning: . E.g. . : image, . : class. . labels. Semi-supervised learning: . . Rob Fergus (New York University). Yair Weiss (Hebrew University). Antonio Torralba (MIT). . Presented by Gunnar Atli Sigurdsson. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: AAAAAAAAAA. 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.. 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. Self-Learning Learning . Technique. . for. Image . Disease. . Localization. . Rushikesh. Chopade1, . Aditya. Stanam2, . Abhijeet. Patil3 & . Shrikant. Pawar4*. 1. Department of . Geology.