Reasoning with temporal information in natural language text has attracted great attention due to its potential applications in summarization, question answering and other tasks. For example, the chronological ordering of events described in a text is important for presenting the information in the summary. Linking information in a natural language text with temporal relations is essential in question answering system to address time sensitive and dynamic world. A crucial first step towards the computational treatment of the temporal information in these applications is the automatic extraction of events described in the text and identification of temporal relations to link these events. Much of the work done in this direction can be classified as -Annotation schemes for identification of events and time implicit in the text, linking the events using temporal relations and Temporal reasoning for solving practical applications.The present paper is a survey of various proposals to address these issues. Various annotation schemas developed to represent temporal information in natural language text. A discussion of the frameworks for temporal reasoning and tractable classes is described. The usefulness of these models to applications such as summarization and question answering are also presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.