Using Signals to Improve Automatic Classification of Temporal Relations
This work addresses the unsolved challenge of temporal relation classification in natural language processing, which is incremental as it builds on existing methods.
The paper tackled the problem of automatically classifying temporal relations between intervals in text by using signal words to improve the accuracy of a recent classification approach, resulting in improved performance.
Temporal information conveyed by language describes how the world around us changes through time. Events, durations and times are all temporal elements that can be viewed as intervals. These intervals are sometimes temporally related in text. Automatically determining the nature of such relations is a complex and unsolved problem. Some words can act as "signals" which suggest a temporal ordering between intervals. In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.