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Natural language processing (NLP) may very broadly be defined as the automatic processing and analysis of large amounts of language data by software. With the evolution and democratization of artificial intelligence and machine learning (AI/ML),as well as data-driven methodologies and theample availability of large amounts of online digitaldata, new sets of NLP tools have emerged and become largely mainstream. However, the key pre-requisite to build applications leveraging these tools and technologies is the availability of highly relevant representative data. Due to this dependence on the context, relevance, quality and availability of a good number of examples to work from,there is often a big challenge increating meaningfuldatasets.

More than 500 years after the invention of Gutenberg’s printing press, technology innovations like digitization and information retrieval represents monumental shift in the way we discover and consume the written word.