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In detail: what Natural Language Processing (NLP) can do

12/15/2021 by Johannes Humbert (LinkedIn | Twitter)

This article is an excerpt from the whitepaper Functionality and areas of operation of Natural Language Processing. Read the full whitepaper here.

  • Speech-to-text and text-to-speech conversion - the conversion of voice commands into computer actions or written text and vice versa. Dictation programmes or voice-controlled systems are well-known examples.
  • Machine translation - Google Translate is probably the best known example. The automatic translation of written or spoken text from one language to another. Translation apps on smartphones are also based on this, as are apps that automatically translate text objects photographed by smartphone (menus in other languages as an example).
  • Sentiment analysis - This is the recognition of moods or subjective opinions in texts. For example, emails can be immediately identified as complaints or support requests, categorised and processed accordingly.
  • Content categorisation - a linguistically based document summary. This includes content search and indexing, content warnings and duplicate detection.
  • Contextual extraction - automatic extraction of structured information from text-based sources - for example, in contracts, different entities such as name, contract duration, notice period, conditions, special clauses, etc.
  • Document summarisation - extensive texts are automatically summarised.
  • Forecasting - based on analysed documents such as contracts, customer communication via email or data captured via OCR, predictions can be made for upcoming or changing customer behaviour patterns.



Your contact

Johannes Humbert
+49 176 83 33 51 46
johannes.humbert@tetrel.ai