Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management |
| Pages | 1815-1818 |
| Number of pages | 4 |
| ISBN (electronic) | 9781450337946 |
| Publication status | Published - 17 Oct 2015 |
| Event | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia Duration: 19 Oct 2015 → 23 Oct 2015 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|---|
| Volume | 19-23-Oct-2015 |
Abstract
Manually inspecting text to assess whether an event occurs in a document collection is an onerous and time consuming task. Although a manual inspection to discard the false events would increase the precision of automatically detected sets of events, it is not a scalable approach. In this paper, we automatize event validation, defined as the task of determining whether a given event occurs in a given document or corpus. The introduction of automatic event validation as a post-processing step of event detection can boost the precision of the detected event set, discarding false events and preserving the true ones. We propose a novel automatic method for event validation, which relies on a supervised model to predict the occurrence of events in a non-annotated corpus. The data for training the model is gathered via crowdsourcing. Experiments on real-world events and documents show that our method (i) outperforms the state-of-the-art event validation approach and (ii) increases the precision of event detection while preserving recall.
Keywords
- Event detection, Event validation, Precision boosting
ASJC Scopus subject areas
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CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015. p. 1815-1818 (International Conference on Information and Knowledge Management, Proceedings; Vol. 19-23-Oct-2015).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Improving event detection by automatically assessing validity of event occurrence in text
AU - Ceroni, Andrea
AU - Gadiraju, Ujwal
AU - Fisichella, Marco
N1 - Publisher Copyright: Copyright 2015 ACM.
PY - 2015/10/17
Y1 - 2015/10/17
N2 - Manually inspecting text to assess whether an event occurs in a document collection is an onerous and time consuming task. Although a manual inspection to discard the false events would increase the precision of automatically detected sets of events, it is not a scalable approach. In this paper, we automatize event validation, defined as the task of determining whether a given event occurs in a given document or corpus. The introduction of automatic event validation as a post-processing step of event detection can boost the precision of the detected event set, discarding false events and preserving the true ones. We propose a novel automatic method for event validation, which relies on a supervised model to predict the occurrence of events in a non-annotated corpus. The data for training the model is gathered via crowdsourcing. Experiments on real-world events and documents show that our method (i) outperforms the state-of-the-art event validation approach and (ii) increases the precision of event detection while preserving recall.
AB - Manually inspecting text to assess whether an event occurs in a document collection is an onerous and time consuming task. Although a manual inspection to discard the false events would increase the precision of automatically detected sets of events, it is not a scalable approach. In this paper, we automatize event validation, defined as the task of determining whether a given event occurs in a given document or corpus. The introduction of automatic event validation as a post-processing step of event detection can boost the precision of the detected event set, discarding false events and preserving the true ones. We propose a novel automatic method for event validation, which relies on a supervised model to predict the occurrence of events in a non-annotated corpus. The data for training the model is gathered via crowdsourcing. Experiments on real-world events and documents show that our method (i) outperforms the state-of-the-art event validation approach and (ii) increases the precision of event detection while preserving recall.
KW - Event detection
KW - Event validation
KW - Precision boosting
UR - http://www.scopus.com/inward/record.url?scp=84958247064&partnerID=8YFLogxK
U2 - 10.1145/2806416.2806624
DO - 10.1145/2806416.2806624
M3 - Conference contribution
AN - SCOPUS:84958247064
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1815
EP - 1818
BT - CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
T2 - 24th ACM International Conference on Information and Knowledge Management, CIKM 2015
Y2 - 19 October 2015 through 23 October 2015
ER -