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Natural Language Processing (NLP) is a Machine Learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.
Common NLP tasks are:

  • Speech recognition: the set of methodologies and technologies that enable the recognition and translation of spoken language into text by computers.
  • Lemmatization: the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word’s lemma, or dictionary form.
  • Part-of-speech (POS) tagging: the process of marking up a word in a text as corresponding to a particular part of speech, based on both its definition and its context. For instance, identify words as nouns, verbs, adjectives, adverbs, etc.
  • Sentiment Analysis: the process of analysing digital text to determine if the emotional tone of the message is positive, negative, or neutral.
  • Named-entity recognition (NER): the process of identifying predefined categories of objects, such as person names, organisations, locations, medical codes, time expressions, quantities, monetary values, percentages, in a body of unstructured text. In other word, NER is the process of taking a string of text and identifying and classifying the entities that refer to each category.
  • Emotion recognition or classification: the task of correctly labelling the main emotion present in a natural language utterance. As for the admissible emotions the set usually used is made by the six basic emotions by Paul Ekman: anger, disgust, fear, happiness, sadness, and surprise.
  • Intent Classification: the task of correctly labelling a natural language utterance from a predetermined set of intents. An example of such a set of intents could be: {Inquiring, Unsubscribe, Purchase, Interested, Demo request, Unsatisfied}.