ABSTRACT
This chapter explores a variety of natural language processing (NLP) applications that use a graph-based approach. Since this field has been burgeoning over time, the chapter explores a non-exhaustive list of applications that range from text summarization, semi-supervised passage retrieval, topic identification, keyword extraction, cross-language information retrieval, and discourse to machine translation, information extraction, term weighting, and question answering.
Each section will provide a detailed view of all the specified approaches with insightful figures and flow diagrams to make it easier for the reader to grasp the valuable information about the sub-topics. As the section correlates the topics with real-world applications, there are various permutations and combinations which can be applied to the topics. Some applications may not be directly implemented, but can be theoretically assumed to be true.
Each application that is considered in this chapter is essential to understanding the detailed workings of NLP. The chapter gathers abundant information from eclectic resources. It also attempts to correlate the sub-topics of NLP using graphical methodologies in industrial applications. Subsequently, the book strives to extrapolate the answers - where these applications are being used, why these approaches are being adopted, and how each use case is being implemented.
