A maximum entropy approach to information extraction from semi-structured and free text
Eighteenth national conference on Artificial intelligence
Adaptive information extraction
ACM Computing Surveys (CSUR)
From Language to Time: A Temporal Expression Anchorer
TIME '06 Proceedings of the Thirteenth International Symposium on Temporal Representation and Reasoning
Composition of conditional random fields for transfer learning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
RADAR: a personal assistant that learns to reduce email overload
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Bayesian information extraction network
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Extracting Event Temporal Information Based on Web
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 01
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Emails are very popular method for information exchange between people. In this paper, an approach to annotate the starting time (stime) and ending time (etime) of duration in schedule notices is proposed. Most related works have reported on only seminar announcements, most of which contain only one schedule per announcement and are written in very restricted format. Different from those seminar announcements, an email frequently contains information about multiple schedules with highly complex format. To process the emails, the proposed system first detects and normalizes all time expressions of the email using regular expression patterns, and then determines which time expression actually represents stime and etime information of schedules. Evaluation is carried out on newly constructed Korean email corpus, and it shows 87.35 % of F1-score for stime and 85.13 % for etime.