The set of all points closest to a given point in a point set than to all other points in the set is an interesting spatial structure called a Voronoi Polygon for the point. The union of all the Voronoi polygons for a point set is called Voronoi Tessellation.

Many applications have been found based on the neighbourhood information provided by this tessellation. The dual of Voronoi tessellation is Delaunay Tessellation, also referred to as Delaunay Triangulation or Triangulated Irregular Network (TIN), which are lines drawn between points where their Voronoi polygons have an edge in common.

Delaunay tessellation is the most fundamental neighbourhood structure because many other important neighbourhood structures, such as, Gabriel Graph, Relative Neighbourhood Graph and Minimal Spanning Tree, can be derived from it.

In research & news articles, keywords form an important component since they provide a concise representation of the article’s content. Keywords also play a crucial role in locating the article from information retrieval systems, bibliographic databases and for search engine optimization. Keywords also help to categorize the article into the relevant subject or discipline.

Conventional approaches of extracting keywords involve manual assignment of keywords based on the article content and the authors’ judgment. This involves a lot of time & effort and also may not be accurate in terms of selecting the appropriate keywords. With the emergence of Natural Language Processing (NLP), keyword extraction has evolved into being effective as well as efficient.

And in this article, we will combine the two — we’ll be applying NLP on a collection of articles (more on this below) to extract keywords.