A Lookup-Free Approach to Knowledge Extraction from News Feeds
James Little, Chris Painter · Research in Computing Science · 2016
Identifying topics without also introducing external assumptions is a major challenge for supervised learning techniques, which by definition classify texts according to precepts.Such approaches identify the presence of preclassified ideas, but cannot identify new ideas.In this paper, we present the results of applying a well understood unsupervised learning technique, in an innovative way, to news feeds analysis.We identify frequent sets of words using the A-Priori algorithm, and grade those sets according to the significance of the Association Rules that they imply.Such sets of words identify common themes in news feeds autonomously, with stopwords as the only added input.We present in detail this methodology and validate it by examining the identified ideas for their ability to identify actual news.