Text-Based Event Temporal Resolution and Reasoning for Information Analytics in Big Data

作者: Junsheng Zhang , Changqing Yao , Peng Qu , Yunchuan Sun

DOI: 10.1109/IIKI.2015.24

关键词:

摘要: Events formulate the world of human being and could be regarded as semantic units in different granularities for information organization. Extracting events temporal from texts plays an important role analytics big data. This paper surveys research work on text-based event resolution reasoning including identification events, resolutions rule-based relation between representations. We point out shortcomings existing future trends advancing establishing/reasoning relations future.

参考文章(42)
Robert J. Gaizauskas, Dragomir R. Radev, Roser Saurí, James Pustejovsky, José M. Castaño, Andrea Setzer, Graham Katz, Robert Ingria, TimeML: Robust Specification of Event and Temporal Expressions in Text New Directions in Question Answering. pp. 28- 34 ,(2003)
G. Altmann, Science and Linguistics Contributions to Quantitative Linguistics. pp. 3- 10 ,(1993) , 10.1007/978-94-011-1769-2_1
Marc B. Vilain, A system for reasoning about time national conference on artificial intelligence. pp. 197- 201 ,(1982)
Bhaskara Marthi, Brian Milch, Stuart Russell, First-Order Probabilistic Models for Information Extraction ,(2003)
Frank Schilder, James Pustejovsky, Graham Katz, Annotating, Extracting and Reasoning about Time and Events ,(2008)
Emmon Bach, The algebra of events Linguistics and Philosophy. ,vol. 9, pp. 324- 333 ,(1986) , 10.1007/BF00627432
Alfio Gliozzo, James Fan, Dirk Hovy, Christopher Welty, Siddharth Patwardhan, When Did that Happen? — Linking Events and Relations to Timestamps conference of the european chapter of the association for computational linguistics. pp. 185- 193 ,(2012)