USFD2: Annotating Temporal Expresions and TLINKs for TempEval-2
This work addresses the problem of temporal information extraction in natural language processing for applications like text analysis, but it is incremental as it builds on existing methods for a specific challenge.
The paper tackled the TempEval-2 challenge by developing USFD2, a system for identifying temporal expressions and relations in text, achieving 90% accuracy in expression type classification and 63% accuracy in event-time relation classification, the second highest score in that task.
We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the automatic identification of temporal entities and relations in text. USFD2 identifies and anchors temporal expressions, and also attempts two of the four temporal relation assignment tasks. A rule-based system picks out and anchors temporal expressions, and a maximum entropy classifier assigns temporal link labels, based on features that include descriptions of associated temporal signal words. USFD2 identified temporal expressions successfully, and correctly classified their type in 90% of cases. Determining the relation between an event and time expression in the same sentence was performed at 63% accuracy, the second highest score in this part of the challenge.