Identifying temporal relations between main events in new articles

With the expansion of the Web 2.0, daily huge amount of data is produced everywhere, namely new articles. These contents need to be exploited in order to extract relevant information and to build knowledge databases. In this concern, processing the temporal dimension of language and extracting temporal information from electronic news articles is becoming a prominent task. In this concern, we propose an approach for identifying inter-sentential temporal relations between main events from news articles. Our approach is based on a complete linguistic analysis of texts and supervised learning models.