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

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