Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
What and Who
Title:Human Computing and Crowdsourcing Methods for Knowledge Acquisition
Speaker:Sarath Kumar Kondreddi
coming from:Max-Planck-Institut für Informatik - D5
Speakers Bio:
Event Type:Promotionskolloquium
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
We use this to send out email in the morning.
Level:Public Audience
Date, Time and Location
Date:Tuesday, 6 May 2014
Duration:60 Minutes
Building:E1 4
Ambiguity, complexity, and diversity in natural language textual
expressions are major hindrances to automated knowledge extraction. As a
result state-of-the-art methods for extracting entities and
relationships from unstructured data make incorrect extractions or
produce noise. With the advent of human computing, computationally hard
tasks have been addressed through human inputs. While text-based
knowledge acquisition can benefit from this approach, humans alone
cannot bear the burden of extracting knowledge from the vast textual
resources that exist today. Even making payments for crowdsourced
acquisition can quickly become prohibitively expensive.
In this thesis we present principled methods that effectively garner
human computing inputs for improving the extraction of knowledge-base
facts from natural language texts. Our methods complement automatic
extraction techniques with human computing to reap the benefits of both
while overcoming each other’s limitations. We present the architecture
and implementation of HIGGINS, a system that combines an information
extraction (IE) engine with a human computing (HC) engine to produce
high quality facts. The IE engine combines statistics derived from large
Web corpora with semantic resources like WordNet and ConceptNet to
construct a large dictionary of entity and relational phrases. It
employs specifically designed statistical language models for phrase
relatedness to come up with questions and relevant candidate answers
that are presented to human workers. Through extensive experiments we
establish the superiority of this approach in extracting
relation-centric facts from text. In our experiments we extract facts
about fictitious characters in narrative text, where the issues of
diversity and complexity in expressing relations are far more
pronounced. Finally, we also demonstrate how interesting human computing
games can be designed for knowledge acquisition tasks.
Name(s):Petra Schaaf
EMail:--email address not disclosed on the web
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):
  • Petra Schaaf, 04/28/2014 11:50 AM -- Created document.