Machine Learning-based Extraction of Keyphrases and its Applications in Multiple Domains

Abstract

ii Preface Raw data of any form conveys no information unless it is processed in some intelligent way. Knowing the most important phrases of textual documents can provide a condensed representation of them which can considerably ease their processing. However, the manual determination of the sets of important phrases for every single document in a large collection of documents is a tedious and expensive task and it often requires expert knowledge. Natural language processing techniques -mostly relying on machine learning -can fortunately help the automatic generation of keyphrases for documents.In this thesis, various models for the extraction of keyphrases from textual documents of various genres and languages are presented, and their potential end-application utilization is demonstrated in the form of a document visualization system. Although most of the earlier studies focused on the domain of scientific papers, we will introduce models for the extraction of keyphrases in two languages (i.e. English and Hungarian) and from various genres including scientific publications, news articles and product reviews as well.