As recalled by Jurafsky and Martin [2008], when we think of Natural Language Processing (NLP), we think of systems like the character HAL from the movie 2001: A Space Odyssey, or IBM Watson [Ferrucci et al., 2010] and its performance on the Jeopardy! TV quiz show. While one is a fictional entity and the other a real one, what matters is that NLP is about the knowledge needed to create intelligent agents capable of understanding and producing natural language. NLP covers a broad range of topics like word and sentence tokenization, text classification, parsing, spelling correction, information extraction, sentiment analysis, topic modeling, meaning extraction, question answering, and summarization. To address these tasks several approaches have been explored along the years, but browsing up-to-date editions of the main NLP forums it is possible to observe the prevalence of data-driven and probabilistic machine learning approaches.

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natural language processing, social sciences