'Information extraction' diaporamas de présentation

807 - TEXT ANALYTICS

1.08k views • 99 slides

Towards a Query Optimizer for Text-Centric Tasks

Towards a Query Optimizer for Text-Centric Tasks. Panagiotis G. Ipeirotis , Eugene Agichtein , Pranay Jain, Luis Gravano. Presenter: Avinandan Sengupta. Session Outline. Text Centric Tasks. Methods Employed. A More Disciplined Approach. Experimental Setup. Proposed Algorithm.

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Sentiment Analysis + MaxEnt *

Sentiment Analysis + MaxEnt *. MAS.S60 Rob Speer Catherine Havasi. * Lots of slides borrowed for lots of sources! See end. . People on the Web have opinions. The world is full of text. Customer verbatims Blogs Comments Reviews Forums. Measuring public opinion through social media?.

831 views • 55 slides

MS Thesis Defense Rohit Raghunathan August 19 th , 2011 Committee Members Dr. Subbarao Kambhampti (Chair) Dr. Joohyun

An Investigation of the cost and accuracy tradeoffs of Supplanting AFDs with Bayes Network in Query Processing in the Presence of Incompleteness in Autonomous Databases. MS Thesis Defense Rohit Raghunathan August 19 th , 2011 Committee Members Dr. Subbarao Kambhampti (Chair)

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Annotation Free Information Extraction

Annotation Free Information Extraction. Chia-Hui Chang Department of Computer Science & Information Engineering National Central University chia@csie.ncu.edu.tw 10/4/2002. Introduction. TEXT IE AutoSlog-TS Semi IE IEPAD.

580 views • 40 slides

Introduction to Information Retrieval

Introduction to Information Retrieval. James Allan Center for Intelligent Information Retrieval Department of Computer Science University of Massachusetts, Amherst. Goals of this talk. Understand the IR problem Understand IR vs. databases Understand basic idea behind IR solutions

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The Future of Semantic Web Search

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Efficient Weight Learning for Markov Logic Networks

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Information Extraction Referatsthemen

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Lecture 1: Introduction

Lecture 1: Introduction. Methods in Computational Linguistics II Queens College. Methods in Computational Linguistics II. 2 nd semester of a two semester course providing instruction in The basics of computer science and programming (via python)

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LING 581: Advanced Computational Linguistics

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Linking Social, Open, and Enterprise Data

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Domain-Independent Data Extraction: Person Names

Domain-Independent Data Extraction: Person Names. Carl Christensen and Deryle Lonsdale Brigham Young University cvchristensen@gmail lonz@byu.edu. Challenge. Extraction software and techniques yield good results with domain specific data extraction

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Generalizing semantic relations

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Amit Satsangi amit@cs.ualberta

Amit Satsangi amit@cs.ualberta.ca. Concept-Based Electronic Health Records: Opportunities and Challenges S. Ebadollahi, S Chang, T. Mahmood, A Coden, A. Amir M. Tanenblatt 14th Annual ACM International Conference on Multimedia (2006). Focus.

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Unix Lecture 6

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Integrity Through Mediated Interfaces PI Meeting March 12, 2002

Integrity Through Mediated Interfaces PI Meeting March 12, 2002. Bob Balzer, Marcelo Tallis Teknowledge <balzer,mtallis>@ teknowledge.com. Legend: Turquoise Changes from July 01 PI meeting. Technical Objectives. Wrap Data with Integrity Marks Insure its Integrity

199 views • 11 slides

Structured Querying of Web Text: A Technical Challenge

Structured Querying of Web Text: A Technical Challenge. Michael J. Cafarella, Christopher Re, Dan Suciu, Oren Etzioni, Michele Banko University of Washington Asilomar, CA January 9, 2007. Structured Queries, Unstructured Data.

191 views • 11 slides