Statistical information retrieval models: Experiments, evaluation on real time data

Abstract

We are all aware of the rise of information age: heterogeneous sources of information and the ability to publish rapidly and indiscriminately are responsible for information chaos. In this work, we are interested in a system which can separate the "wheat" of vital information from the chaff within this information chaos. An efficient filtering system can accelerate meaningful utilization of knowledge. Consider Wikipedia, an example of community-driven knowledge synthesis. Facts about topics on Wikipedia are continuously being updated by users interested in a particular topic. Consider an automatic system (or an invisible robot) to which a topic such as "President of the United States" can be fed. This system will work ceaselessly, filtering new information created on the web in order to provide the small set of documents about the "President of the United States" that are vital to keeping the Wikipedia page relevant and up-to-date. In this work, we present an automatic information filtering system for this task. While building such a system, we have encountered issues related to scalability, retrieval algorithms, and system evaluation; we describe our efforts to understand and overcome these issues