What is Natural Language Processing?

A short article about a subfield in the field of artificial intelligence

When deciding on approaches that can be taken to working within the field of AI one venue that can be taken is the communication with human beings.

  1. Natural language understanding.
  2. Natural language generation.
  • Text-to-speech. Given a text, transform those units and produce a spoken representation.
  • Sentiment analysis. Extract subjective information usually from a set of documents, often using online reviews to determine “polarity” about specific objects. It is especially useful for identifying trends of public opinion in social media, for marketing.
  • Topic segmentation and recognition. Given a chunk of text, separate it into segments each of which is devoted to a topic, and identify the topic of the segment.
  • Automatic summarization. Produce a readable summary of a chunk of text. Often used to provide summaries of the text of a known type, such as research papers, articles in the financial section of a newspaper.
  • Coreference resolution. Given a sentence or larger chunk of text, determine which words (“mentions”) refer to the same objects (“entities”).
  • Discourse analysis. This rubric includes several related tasks. One task is identifying the discourse structure of a connected text, i.e. the nature of the discourse relationships between sentences (e.g. elaboration, explanation, contrast). Another possible task is recognizing and classifying the speech acts in a chunk of text (e.g. yes-no question, content question, statement, assertion, etc.).

Written by

AI Policy and Ethics at www.nora.ai. Student at University of Copenhagen MSc in Social Data Science. All views are my own. twitter.com/AlexMoltzau

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