Signal: Advanced Real-Time Information Filtering

Abstract

The overload of textual information is an ever-growing problem to be addressed by modern information filtering systems, not least because strategic decisions are heavily influenced by the news of the world. In particular, business opportunities as well as threats can arise by using up-to-date information coming from disparate sources such as articles published by global news providers but equally those found in local newspapers or relevant blogposts. Common media monitoring approaches tend to rely on large-scale, manually created boolean queries. However, in order to be effective and flexible in a business environment, user information needs require complex, adaptive representations that go beyond simple keywords. This demonstration illustrates the approach to the problem that Signal takes: a cloud-based architecture that processes and analyses, in real-time, all the news of the world and allows its users to specify complex information requirements based on entities, topics, industry-specific terminology and keywords.

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.