Automatic system for identifying and categorizing temporal relations in natural language

Nowadays, the automatic processing of digitalized documents is crucial to cope with the increasing amount of information available. This issue is addressed from the natural language processing (NLP) research field. One of the tasks required for many NLP applications is temporal information processing. It involves the automatic extraction and interpretation of temporal expressions, events, and their relations. Specifically, the identification and the categorization of temporal relations are the most complex subtasks yet to solve, judging from the results reported in the latest international evaluation exercise. Temporal relation identification has been addressed by very few approaches, and the current categorization approaches are still not a definitive solution. This paper presents a system that approaches temporal relation identification and categorization. The former is approached with a knowledge‐driven strategy and the later with data‐driven strategy based on different machine‐learning techniques. Our proposal has been empirically evaluated over the currently available English data sets annotated with temporal information (TimeBank and AQUAINT) in a 10‐fold cross‐validated experiment. The results obtained support that the presented approach achieves a high performance. It improves the baseline F1 by 46% and outperforms the state of the art. © 2012 Wiley Periodicals, Inc.