Temporal search in document streams

In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on termmatching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query. On the other hand, time-only-based methods fall short when it comes to reasoning about events in social media. During the last few years users create chronologically ordered documents about topics that draw their attention in an ever increasing pace. However, with the vast adoption of social media, new types of marketing campaigns have been developed in order to promote content, i.e. brands, products, celebrities, etc

A Burstiness-aware Approach for Document Dating

A large number of mainstream applications, like temporal search, event detection, and trend identification, assume knowledge of the timestamp of every document in a given textual collection. In many cases, however, the required timestamps are either unavailable or ambiguous. A characteristic instance of this problem emerges in the context of large repositories of old digitized documents. For such documents, the timestamp may be corrupted during the digitization process, or may simply be unavailable. In this paper, we study the task of approximating the timestamp of a document, so-called document dating. We propose a content-based method and use recent advances in the domain of term burstiness, which allow it to overcome the drawbacks of previous document dating methods, e.g. the fix time partition strategy. We use an extensive experimental evaluation on different datasets to validate the efficacy and advantages of our methodology, showing that our method outperforms the state of the art methods on document dating