Authors:
Masnizah Mohd
and
Kiyoaki Shirai
Affiliation:
Japan Advanced Institute of Science and Technology, Japan
Keyword(s):
Uncertainty, Named Entities, User, Event Detection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Studies, Metrics & Benchmarks
;
Symbolic Systems
Abstract:
Ambiguous information contributes to the uncertainty issue. Type of information such as using named entities
has been proved to provide significant information to the user compared to the ‘bag-of-words’ in identifying
an event. So what else could contribute to the uncertainty in an event detection? We propose to answer this
question by analysing the distribution of named entities across topics, and explore the potential of named
entities in a user experiment. We construct an event detection task with 20 users and use news dataset from
Topic Detection and Tracking (TDT) corpus, under the Sports and Politics categories. We analyse the results
from five uncertainty dimensions: too little information, too much information, complex information,
ambiguous information and conflicting information. These dimensions are categorise as two factors; amount
and type of information. There was no statistical significance difference in the amount of information given
with the number of successful eve
nt detected. However, with little information and high named entities has
contributes in reducing uncertainty. In addition, the amount of information and information quality are
mutually independent. Our results suggest that uncertainty vary substantially between the amount of
information and type of information in event detection.
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