Campus Event Calendar
Event Entry
What and Who
Time for Text Mining and Information Retrieval
Jannik Strötgen
MMCI
Joint Lecture Series
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Wednesday, 11 January 2017
12:15
60 Minutes
E1 5
002
Saarbrücken
Abstract
Temporal information is an important aspect in any information space and
has important characteristics that make it highly eligible to be
exploited in diverse text mining and information retrieval scenarios.
For quite a long time, only the metadata of text documents (e.g.,
publication dates) has been considered, but with recent improvements in
natural language processing (NLP), temporal expressions occurring in the
content of documents (e.g., "January 2016", "next week") can be
extracted and interpreted efficiently with high quality. This is
particularly valuable as in many types of documents, temporal
expressions do not only occur frequently but also play a crucial role,
e.g., to anchor events in time.
In this talk, after explaining the basics and challenges of the NLP task
"temporal tagging", I present our approach to enrich documents with
temporal information. Then, I showcase several application scenarios in
which we exploit automatically extracted temporal annotations for search
and exploration tasks. These range from temporal information retrieval
for news articles, via Wikipedia-based event extraction, up to the
exploration of fictitious happenings in literary texts in the context of
digital humanities research.
has important characteristics that make it highly eligible to be
exploited in diverse text mining and information retrieval scenarios.
For quite a long time, only the metadata of text documents (e.g.,
publication dates) has been considered, but with recent improvements in
natural language processing (NLP), temporal expressions occurring in the
content of documents (e.g., "January 2016", "next week") can be
extracted and interpreted efficiently with high quality. This is
particularly valuable as in many types of documents, temporal
expressions do not only occur frequently but also play a crucial role,
e.g., to anchor events in time.
In this talk, after explaining the basics and challenges of the NLP task
"temporal tagging", I present our approach to enrich documents with
temporal information. Then, I showcase several application scenarios in
which we exploit automatically extracted temporal annotations for search
and exploration tasks. These range from temporal information retrieval
for news articles, via Wikipedia-based event extraction, up to the
exploration of fictitious happenings in literary texts in the context of
digital humanities research.
Contact
Jennifer Müller
2900
--email hidden
passcode not visible
logged in users only
Jennifer Müller, 09/12/2016 15:48 -- Created document.