'Natural language text' presentation slideshows

Introduction to NLP

Introduction to NLP. Heng Ji jih@rpi.edu September 3, 2014. Outline. Course Overview Introduction to NLP Introduction to Watson Watson in NLP View Machine Learning for NLP Why NLP is Hard. Homepages. Course homepage http:// nlp.cs.rpi.edu/course/fall14/nlp.html My homepage

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Opportunities in Natural Language Processing

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Lecture 1 Overview

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The role of OCL in the Model Driven Architecture

The role of OCL in the Model Driven Architecture. Jos Warmer Klasse Objecten J.Warmer@klasse.nl http://www.klasse.nl. Part 1. The role of modeling in software development. Modeling Maturity Levels. MML 0: No specification MML 1: Textual Specification MML 2: Text with Models

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592 views • 49 slides

Foundations IV: Ontology Evolution and Knowledge Management Class Session 7

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Unsupervised Semantic Parsing

Unsupervised Semantic Parsing. Hoifung Poon and Pedro Domingos EMNLP 2009 Best Paper Award Speaker: Hao Xiong. Outline. Motivation Unsupervised semantic parsing Learning and inference Conclusion. Semantic Parsing.

633 views • 39 slides

Text Languages and Properties

Text Languages and Properties. Berlin Chen 2003. Reference: 1. Modern Information Retrieval, chapter 6. Documents. A document is a single unit of information Typical text in digital form, but can also include other media Two perspectives Logical View

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CS60057 Speech &Natural Language Processing

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Stemming And Lemmatization Tutorial | Natural Language Processing (NLP) With Python | Edureka

( **Natural Language Processing Using Python: - https://www.edureka.co/python-natural... ** ) This PPT will provide you with detailed and comprehensive knowledge of the two important aspects of Natural Language Processing ie. Stemming and Lemmatization. It will also provide you with the differences between the two with Demo on each. Following are the topics covered in this PPT: Introduction to Big Data What is Text Mining? What is NLP? Introduction to Stemming Introduction to Lemmatization Applications of Stemming & Lemmatization Difference between stemming & Lemmatization Follow us to never miss an update in the future. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

1.09k views • 26 slides

A Pipelined Architecture

Document Planning. Document Plan. A Pipelined Architecture. Microplanning. Text Specification. Surface Realisation. Semantic specification. TACTICAL GENERATOR. KPML. semantics. lexicogrammar. sentence. Semantic specification. TACTICAL GENERATOR. KPML is a. Process. generation

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Motivation

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Using answer set programming for knowledge representation and reasoning: future directions

Using answer set programming for knowledge representation and reasoning: future directions. Chitta Baral Arizona State University. Apologies. For not being here physically on this special occasion. My early history with SMS. My Ph.D during 1987-1991

261 views • 18 slides

Applications of NLP in determining Tag Redundancy in Folksonomies

Applications of NLP in determining Tag Redundancy in Folksonomies. Progress Report, November 9, 2009 Tom Schimoler. Big Question:. What is redundancy?

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Exploiting Discourse Structure for Sentiment Analysis of Text

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Exploiting Emoticons in Sentiment Analysis

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Applications of Text Mining

Applications of Text Mining. Ewan Klein School of Informatics & NeSC. Text Mining. Goals Extract useful information from large bodies of unstructured or semi-structured documents Looks for patterns in natural language text Driven by application needs Three Areas: Adding Metadata

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Continued from Part 1 of the Conversations Wars series which talks about the overall landscape of conversational products and the higher maturity in the enterprise space as opposed to the much spoken-about consumer space.

35 views • 3 slides