A Corpus-based Study of Temporal Signals
This work addresses the challenge of temporal relation extraction in natural language processing, but it is incremental as it focuses on analyzing existing data without introducing new methods or broad improvements.
The study tackled the problem of automatic temporal ordering of events in discourse by investigating the role of temporal signals, such as 'before' or 'after', and provided a quantitative analysis of these expressions in the TimeBank corpus.
Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are conveyed in text via va rious linguistic mechanisms including the use of expressions such as "before", "after" or "during" that explicitly assert a temporal relation -- temporal signals. In this paper, we investigate the role of temporal signals in temporal relation extraction and provide a quantitative analysis of these expres sions in the TimeBank annotated corpus.