Florian Kunneman

Florian Kunneman PhD

Postdoc Centre for Language Studies since Dec. 12, 2011
E4.06 024-36015731 f.kunneman@let.ru.nl @fkunneman

Research Projects

ADNEXT

ADNEXT

Dec. 12, 2011 -- July 31, 2016 http://www.commit-nl.nl/projects/wp-packages/adaptive-information-extraction-over-time-adnext Antal van den Bosch , Florian Kunneman , Ali Hürriyetoğlu , Mustafa Erkan Başar , Matje van de Camp

The objective of ADNEXT (ADaptive informatioN EXtraction over Time) is to develop trainable, adaptable Dutch language information extraction technology for named entity recognition, event detection, and time identification. The technology has a broad coverage “default” mode and retrains dynamically to new domains upon being confronted with new (clusters of) news or user-generated data (such as Twitter).

Dream research

Dream research

June 2, 2014 -- Antal van den Bosch , Maarten van Gompel , Florian Kunneman , Ali Hürriyetoğlu , Folgert Karsdorp , Iris Hendrickx , Martin Reynaert , Wessel Stoop , Louis Onrust

Dreams, the involuntary perceptions that occur in our minds during sleep, have been the topic of studies in many fields of research, including psychiatry, psychology, neurobiology, and religious studies. Their narrative content also links dreams to other forms of storytelling, with sharp distinctions (such as the focus on one's personal life and the typical personal perspective) but also interesting overlaps with genres such as orally transmitted folktales. We present a study on dreams aimed at the large-scale analysis of dreams using text analytics.

Publications

F. Kunneman, C. Liebrecht, M. Van Mulken, and A. Van den Bosch
Signaling sarcasm: From hyperbole to hashtag
Information Processing \& Management, 51(4), 2015
RIS, BibTex
F. Kunneman and A. van den Bosch
Automatically identifying periodic social events from Twitter
Proceedings of Recent Advances in Natural Language Processing 2015, 2015
Full text (external), RIS, BibTex
F. Kunneman, C. Liebrecht, and A. van den Bosch
The (un)predictability of emotional hashtags in Twitter
Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM), Association for Computational Linguistics, 2014
Full text (external), RIS, BibTex
F. Kunneman, A. Hürriyetoglu, N. Oostdijk, and A. van den Bosch
Timely identification of event start dates from Twitter
Computational Linguistics in the Netherlands Journal, 4, 2014
RIS, BibTex
F. Kunneman and A. van den Bosch
Event detection in Twitter: A machine-learning approach based on term pivoting
Proceedings of the 26th Benelux Conference on Artificial Intelligence, 2014
RIS, BibTex
F. Kunneman, C. Liebrecht, M. van Mulken, and A. van den Bosch
Signaling sarcasm: From hyperbole to hashtag
Information Processing and Management, 2014
Full text (external), RIS, BibTex
C. Liebrecht, F. Kunneman, and A. van den Bosch
The perfect solution for detecting sarcasm in tweets #not
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, 2013
Full text (external), RIS, BibTex
A. Hürriyetoglu, F. Kunneman, and A. van den Bosch
Estimating the time between Twitter messages and future events"
Proceedings of the 13th Dutch-Belgian Information Retrieval Workshop, 2013
RIS, BibTex
H. Tops, A. van den Bosch, and F. Kunneman
Predicting time-to-event from Twitter messages
Proceedings of the 25th Benelux Artificial Intelligence Conference, 2013
RIS, BibTex
F. Kunneman and A. van den Bosch
Leveraging unscheduled event prediction through mining scheduled event tweets
Proceedings of the 24th Benelux Conference on Artficial Intelligence, 2012
RIS, BibTex

Software

Lama Events

Lama Events

by Antal van den Bosch , Florian Kunneman , Ali Hürriyetoğlu , Mustafa Erkan Başar http://applejack.science.ru.nl/lamaevents/

Lama Events is a calendar application listing events in the near future. The events are detected and selected by a fully automatic procedure in the Dutch Twitter stream (courtesy of Twiqs.nl). Tweets referring to the same future events are clustered based on the frequent co-occurrence of words (names, phrases) and temporal expressions that characterize the event. The date and time of the event is automatically determined based on direct and indirect time references in the texts of the tweets in a cluster. The demo shows a day-by-day ranked list of automatically detected events in the Dutch language area (Netherlands and Flanders).