PPT-NLP Introduction to NLP Semantic Parsing

Author : calandra-battersby | Published Date : 2019-06-22

Semantic Parsing Converting natural language to a logical form eg executable code for a specific application Example Airline reservations Geographical query systems

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NLP Introduction to NLP Semantic Parsing: Transcript

Semantic Parsing Converting natural language to a logical form eg executable code for a specific application Example Airline reservations Geographical query systems Stages of Semantic Parsing. Semi-supervised . dependency parsing. Supervised parsing . Training: Labeled data. Semi-supervised parsing. Training: Additional unlabeled data + labeled data. Unlabeled data. Labeled data. Semi-supervised Parsing. Prof. O. . Nierstrasz. Thanks to Jens Palsberg and Tony Hosking for their kind permission to reuse and adapt the CS132 and CS502 lecture notes.. http://www.cs.ucla.edu/~palsberg/. http://www.cs.purdue.edu/homes/hosking/. Top-down vs. bottom-up parsing. Top-down . vs. bottom-up . parsing. Ex. Ex. Ex. Ex. +. Nat. *. Nat. Nat. Ex. Ex. . . . Nat. | . (. Ex. ). | . Ex. . +. . Ex. | . Ex. . *. . Ex. Matched input string. Niranjan Balasubramanian. March 24. th. 2016. Credits: . Many slides from:. Michael Collins, . Mausam. , Chris Manning, . COLNG 2014 Dependency Parsing Tutorial, . Ryan McDonald, . . Joakim. . Nivre. Top-down versus Bottom-up Parsing. Top down:. Recursive descent parsing. LL(k) parsing. Top to down and leftmost derivation . Expanding from starting symbol (top) to gradually derive the input string. CS 4705. Julia Hirschberg. 1. Some slides adapted from Kathy McKeown and Dan Jurafsky. Syntactic Parsing. Declarative . formalisms like CFGs, FSAs define the . legal strings of a language. -- but only tell you whether a given string is legal in a particular language. ,. SEMANTIC ROLE . LABELING, SEMANTIC PARSING. Heng. . Ji. jih@rpi.edu. September 17, . 2014. Acknowledgement: . FrameNet. slides from Charles . Fillmore;. Semantic Parsing Slides from . Rohit. Kate and Yuk . S . -> NP VP. NP -> DT N | NP PP. PP -> PRP NP. VP -> V NP | VP PP. DT -> 'a' | 'the'. N -> 'child' | 'cake' | 'fork'. PRP -> 'with' | 'to'. Parser. Earley. parser. Problems with left recursion in top-down parsing. VP . . VP PP. Background. Developed by Jay Earley in 1970. No need to convert the grammar to CNF. Left to right. Complexity. Jimmy Lin. The . iSchool. University of Maryland. Wednesday, September 2, 2009. NLP. IR. About Me. Teaching Assistant: . Melissa Egan. CLIP. About You (pre-requisites). Must be interested in NLP. Must have strong computational background. Parsing Giuseppe Attardi Dipartimento di Informatica Università di Pisa Università di Pisa Question Answering at TREC Consists of answering a set of 500 fact-based questions, e.g. “When was Mozart born Jing Jiang. March 20, 2018. CICLing. Background. Recent years have witnessed a fast-growing trend of using deep learning solutions, oftentimes end-to-end, for NLP tasks.. Machine translation. Information extraction. the Department set of this grammar the grammar in which very powerful of movinga new AcknowledgmentsI am very grateful to the many people who have influenced this research and madethis thesis possible Introduction. Semantic Role Labeling. Agent. Theme. Predicate. Location. Can we figure out that these have the same meaning?. XYZ . corporation . bought. the . stock.. They . sold. the stock to XYZ .

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