Leveraging temporal expressions for segmented-based information retrieval

The extraction of temporal information from text documents is becoming increasingly important in many applications such as natural language processing, information retrieval, question answering, etc. Indeed, the temporal dimension plays a key role on most of these systems, promoting better performance. Our goal is the definition of a temporal document representation, incorporating the time dimension into information retrieval model to improve the quality of the results. Our approach is based on temporal segmentation of documents. Temporal-aware retrieval models may explore a richer temporal document representation, enabled by segmentation. To achieve this, first we must identify temporal expressions and capture, when possible, their normalized time values. Starting from our prior work on temporal expressions recognition, we present in this paper, a resolution tool that achieves promising results in a Portuguese collection. Furthermore, a temporal characterization of the used collection shows enough and suitable information for a meaningful temporal document segmentation.