PPT-Natural Language

Processing in the age of twitter and facebook Richárd Farkas University of Szeged Infocommunication technologies and the society of future FuturICThu TÁMOP422C111KONV20120013. Language Processing. Lecture . 3. Albert . Gatt. 1. LIN3022 Natural Language Processing. Reminder: Non-deterministic FSA. An FSA where there can be multiple paths for a single input (tape).. Two . basic approaches . Ray Mooney. Department of Computer Science. University of Texas at Austin. Joint work with. David Chen. Joohyun. . Kim. Machine Learning and . Natural Language Processing (NLP). Manual software development of robust NLP systems was found to be very difficult and time-consuming.. using SIRA Technology. Chuck . Rehberg. CTO . at . Trigent. Software . and. Chief . Scientist at Semantic Insights. ™. The Big Mechanism Vision. Big Mechanisms are causal, explanatory models of complicated systems in which interactions have important causal effects. The collection of Big Data is increasingly automated, but the creation of Big Mechanisms remains a human endeavor made increasingly difficult by the fragmentation and distribution of knowledge. To the extent that we can . Class Logistics. Quiz. Where is this quote from?. Dave Bowman. : Open the pod bay doors, HAL.. HAL. : I’m sorry Dave. I’m afraid I can’t do that.. Quiz Answer. “2001: A Space Odyssey” . 1968 film by Stanley Kubrick . Neural Networks from Scratch. Presented . By. Wasi Uddin . Ahmad. 3. rd. November, 2016. Written By. Denny . Britz. http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/. "Lane, Mary E. . Commonsense Reasoning. Ernest Davis. New York Amateur Computer Club. May 14, 2015. This is Anne and her babysitter.. This is Anne and her babysitter.. Which is which?. Commonsense Reasoning. Can you make a salad out of a polyester shirt?. 1 Learning Natural Language from its Perceptual Context Ray Mooney Department of Computer Science University of Texas at Austin Joint work with David Chen Joohyun Kim Machine Learning and Natural Language Processing (NLP) What is NLP?. “Natural” languages. English, . Hindi, . French, Swahili, Arabic, . Bangla, . ….. NOT Java, C++, Perl, …. Ultimate goal: Natural human-to-computer communication. Sub-field of Artificial Intelligence, but very interdisciplinary. Giuseppe Attardi. Dipartimento. . di. . Informatica. Università. . di. Pisa. Università di Pisa. Goal of NLP. Computers would be a lot more useful if they could handle our email, do our library research, chat to us …. 利 害 . li-hai. benefit-harm. standard selects social discourse . 道. . dao. guide. . Everyone's guiding rules, attitudes norms. Reform tradition--shocking results. . moral reform impasse. Partial solution: Rule out collectively self-defeating moralities. Abstract We discuss ways of allowing the users of a natural language processor to define, examine, and modify the definitions of any domain-specific words or phrases known to the system. An implement The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand First Assignment. To be released over the weekend (due within the following week). 1. Today . What is Natural Language Processing?. Why is it hard? . Common Tasks in NLP. Language Modeling. Word and Sentence representations for ML.