Terms in Time and Times in Context : A Graph-based Term-Time Ranking Model

Abstract

Approaches in support of the extraction and exploration of temporal information in documents provide an important ingredient in many of today's frameworks for text analysis. Methods range from basic techniques, primarily the extraction of temporal expressions and events from documents, to more sophisticated approaches such as ranking of documents with respect to their temporal relevance to some query term or the construction of timelines. Almost all of these approaches operate on the document level, that is, for a collection of documents a timeline is extracted or a ranked list of documents is returned for a temporal query term. In this paper, we present an approach to characterize individual dates, which can be of different granularities, and terms. Given a query date, a ranked list of terms is determined that are highly relevant for that date and best summarize the date. Analogously, for a query term, a ranked list of dates is determined that best characterize the term. Focusing on just dates and single terms as they occur in documents provides a fine-grained query and exploration method for document collections. Our approach is based on a weighted bipartite graph representing the co-occurrences of time expressions and terms in a collection of documents. We present different measures to obtain a ranked list of dates and terms for a query term and date, respectively. Our experiments and evaluation using Wikipedia as a document collection show that our approach provides an effective means in support of date and temporal term summarization.