PPT-Generating Natural Language

Generating Natural Language Descriptions from Structured Data Preksha Nema Shreyas Shetty Parag Jain Anirban Laha   Karthik Sankaranarayanan Mitesh Khapra IBM Research Indian Institute of Technology Madras India. 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.. Ray Mooney. Department of Computer Science. University of Texas at Austin. Joint work with. Niveda. . Krishnamoorthy. . Girish. . Malkarmenkar. Tanvi. . Motwani. .. Kate . Saenko. 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. . :. Tracking the Electricity Sector Transition and What It Means for the Nation. Approach. Look back: . Compared coal fleet operational in 2008 with coal fleet operational in 2016, flagging retirements and natural-gas conversions. . 1 Generating Natural-Language Video Descriptions Using Text-Mined Knowledge Ray Mooney Department of Computer Science University of Texas at Austin Joint work with Niveda Krishnamoorthy Girish 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. describing a class, a member method or a member attribute of a class, using a conversational front end. Then the input is fed into an augmented transition network (ATN) 1 for parsing and semantic anal 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 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.