LiveDoc: Showing Contextual Information Using Topic Modeling Techniques

Abstract

We present a solution named LiveDoc, which augments natural language text documents with relevant contextual background information. This background information helps readers to understand the context of the discourse better by fetching relevant information from other sources such as Wikipedia. Often the readers do not possess all background and supplementary information required for comprehending the purport of a narrative such as a news op-ed article. At the same time, it is not possible for authors to provide all contextual information while addressing a particular topic. LiveDoc processes the information in a document; uses extracted entities to fetch relevant background information in the context of the document from various sources (as defined by user) using semantic matching and topic modeling techniques like Latent Dirichlet Allocation and Hierarchical Dirichlet Process; and presents the background information to the user by augmenting the original document with the fetched information. Reader is then equipped better to understand the document with this additional background information. We present the effectiveness of our solution through extensive experimentation and associated results.