• Home
  • Blog
  • People
  • Projects
  • Publications
  • Seminars
  • DSR Expo
  • Courses

Data Science Research

Menu
  • Home
  • Blog
  • People
  • Projects
  • Publications
  • Seminars
  • DSR Expo
  • Courses

CAMeL: Crowd Assisted Machine Learning

CAMeL: Crowd Assisted Machine Learning

 

CAMeL_Logo

The trade-offs between human and mechanical computation are well known.  While machines are cheaper and quicker than humans, on most tasks they are still far less accurate.  Crowdsourcing through services like Amazon Mechanical Turk bridge this gap somewhat by making human computation less costly and more available, though large scale or repetitious tasks may still prove costly.  Ideally, we want to automate most aspects of our task and only bring in humans when they’re really needed was bedeutet bei deezer herunterladen.

CAMeL (Crowd-Assisted Machine Learning) is a paradigm that takes a data cleaning approach to the use of human computation.  Allow automated methods to perform the task to the best of their abilities and let humans “clean up” the most erroneous or uncertain aspects.  The research challenges become how to best decompose the machine learning problem to be solvable by many micro-tasks in parallel, how to optimize over the number of questions asked, and how best to present information and elicit feedback from the crowd.  We have built a number of systems to address these challenges listen file to 7 letters.

CASTLE: Crowd-Assisted Information Extraction System

CastleLogo

CASTLE is a crowd-assisted information extraction system (IE) based on statistical machine learning.  It uses a conditional random field (CRF) to annotate an initial batch of text data.  In contrast to other IE systems, however, CASTLE uses a probabilistic data model to store the results, automatically executes crowdsourcing to correct the most uncertain results, and integrates their responses back into the probabilistic data model fritz schach download for free.

Pi-CASLE: Probabilistically Integrated CASTLE

PiCastleLogo

Pi-CASTLE (Probabilistically Integrated CASTLE) is an extension of CASTLE with a number of enhancements centered around probabilistic integration.  It expands upon the data model of the original CASTLE system by pushing all operations into the database implemented as user-defined functions.  Additionally, Pi-CASTLE contains a more robust quality control mechanism compared to its counterpart.  We implemented a novel Bayesian scheme that maps crowd responses to probabilities and combines them before integration back into the DB windows 10 free download 64 bit.

CAKE

The CAKE (Crowd-Assisted Knowledge Extraction) system is currently under development.  The goal is to automate the data cleaning process that occurs over a knowledge base (KB) using the crowd.  The previous CASTLE systems contained only text annotations, but CAKE is a fully probabilistic KB containing facts, relations, and rules for generating new facts.  We use human computation to improve all three aspects of the KB, cleaning up as well as generating new facts and relations, and even using human ingenuity to create new rules that govern the data download the keyboard.

 

Faculty: Daisy Zhe Wang
Students: Sean Goldberg, Christan Grant
Collaborators: Tim Kraska

Publications

  • CASTLE: Crowd-Assisted System for Textual Labeling & Extraction
    Sean Goldberg, Daisy Zhe Wang, Tim Kraska
    Proceedings of AAAI HCOMP 2013
  • Pi-CASTLE: A Probabilistically Integrated System for Crowd-Assisted Textual Labeling & Extraction
    Sean Goldberg, Daisy Zhe Wang, Christan Grant
    Accepted to ACM JDIQ, 2016

Sponsors

NIST

Adobe_Logo

DTCC

pcori uf-clinical

ICHP

Recent Posts

Recent Posts

  • A Brief Overview of Weak Supervision
  • DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
  • IDTrees Data Science Challenge: 2017
  • Efficient Conditional Rule Mining over Knowledge Bases
  • Taming The Data Monster To Make Better Decisions

Related Blogs

  • ampLab
  • Data Beta
  • Fast ML

Post Categories

Categories

  • courses
  • ecology
  • NIST and open eval
  • publications
  • research
  • research directions
  • survey
  • Uncategorized

Archives

Archives

  • October 2020
  • December 2019
  • April 2019
  • December 2018
  • August 2018
  • February 2018
  • November 2017
  • June 2017
  • May 2017
  • March 2017
  • December 2016
  • October 2016
  • April 2016
  • March 2016
  • December 2015
  • November 2015
  • October 2015
  • May 2015
  • November 2014
  • October 2014
  • July 2014
  • May 2014
  • March 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013

Meta

DSR Wiki
Site Admin
WordPress.org
  • Whsmith Lodgers Agreement
  • What To Ask For In A Prenuptial Agreement
  • What Is Department Of State Corporation Bureau Or Business Partnership Agreement
  • What Is A General Security Agreement Nz
  • What Agreement Led To The Establishment Of The Euro A Common European Currency Quizlet
  • Vmware Service Provider License Agreement
  • Validity Of Debt Agreement In India
  • University Of Manitoba Unifor Collective Agreement
  • U.s.-China Trade Agreement 1999
  • Training Agreement Plan Definition
  • Thoroughbred Lease Agreement
  • The Canada-Us-Mexico Agreement Enters Into Force July 1
  • Td Ameritrade Brokerage Agreement
  • Subscription Service Agreements
  • Subject And Verbs Agreement
  • Standard Non Disclosure Agreement Australia
  • Source Code Development Agreement
  • Simple One Page Room Rental Agreement Pdf
  • Shareholders Agreements Sweet Maxwell
  • Service Purchase Agreement Meaning