The recent decades have witnessed an unprecedented expansion in the volume of
unstructured data in digital textual formats. Companies are now starting to recognize the …

'Fascinating'-Financial Times Algorithms are running our society, and as the Cambridge
Analytica story has revealed, we don't really know what they are up to. Our increasing …

This paper proposes two natural language processing models for extracting useful
information from multilingual, unstructured (free form) CV documents. The model identifies …

Researchers and industry developers in artificial intelligence (AI) and natural language
processing (NLP) have uniformly adopted a Rawlsian definition of fairness. On this …

Extracting information from r\'esum\'es is typically formulated as a two-stage problem, where
the document is first segmented into sections and then each section is processed …

Abstract Named Entity Recognition (NER), Part-of-Speech (POS) tagging and Dependency
Parsing are tasks that are central to Natural Language Processing (NLP) because they …

Word embedding is a popular framework that represents text data as vectors of real
numbers. These vectors capture semantics in language, and are used in a variety of natural …

Automatic analysis of curriculum vitae (CVs) of applicants is of tremendous importance in
recruitment scenarios. The semi-structuredness of CVs, however, makes CV processing a …

Automatic text generation has redefined the act of content generation in multiple fields such
as article/news summarization, chatbots, and virtual assistants. For a person on the job …

The proliferation of textual data in the form of online news articles and social media feeds
has had an impact on the text analytics developments in recent years. Some of the …