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:Joint Models for Information and Knowledge Extraction
Speaker:Dat Ba Nguyen
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:Friday, 1 December 2017
Duration:60 Minutes
Building:E1 4
Information and knowledge extraction from natural language text is a key asset

for question answering, semantic search, automatic summarization, and other machine reading
applications. There are many sub-tasks involved such as named entity recognition, named entity disambiguation,
co-reference resolution, relation extraction, event detection, discourse parsing, and others. Solving these tasks is
challenging as natural language text is unstructured, noisy, and ambiguous. Key challenges, which focus on identifying
and linking named entities, as well as discovering relations between them, include:
• High NERD Quality. Named entity recognition and disambiguation, NERD for short, are preformed first in the
extraction pipeline. Their results may affect other downstream tasks.
• Coverage vs. Quality of Relation Extraction. Model-based information extraction methods achieve high extraction
quality at low coverage, whereas open information extraction methods capture relational phrases between entities.
However, the latter degrades in quality by non-canonicalized and noisy output. These limitations need to be overcome.
• On-the-fly Knowledge Acquisition. Real-world applications such as question answering, monitoring content streams, etc.
demand on-the-fly knowledge acquisition. Building such an end-to-end system is challenging because it requires high
throughput, high extraction quality, and high coverage.
This dissertation addresses the above challenges, developing new methods to advance the state of the art. The first
contribution is a robust model for joint inference between entity recognition and disambiguation. The second
contribution is a novel model for relation extraction and entity disambiguation on Wikipedia-style text. The third
contribution is an end-to-end system for constructing query-driven, on-the-fly knowledge bases.

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/AG5/MPII/DE, 11/21/2017 10:17 AM
Last modified:
halma/MPII/DE, 02/15/2018 12:01 AM
  • Petra Schaaf, 11/21/2017 10:20 AM -- Created document.