Retrieving Information from Multiple Sources

Abstract

The Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important to retrieve information from multiple sources. We propose a novel neural network based model which integrates the embeddings from multiple sources, and thus retrieves information from them jointly, %all the sources together, as opposed to combining multiple retrieval results. The importance of the proposed model is that no document-aligned comparable data is needed. Experiments on posts related to a particular event from three different sources - Facebook, Twitter and WhatsApp - exhibit the efficacy of the proposed model.