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Applications

Wassim edited this page Jul 21, 2023 · 7 revisions

Content

  • pre-trained data baked into the model
  • Context provided content as Text
  • Context provided content as Embeddings
  • Fine tuning
  • pre-filtering and pre-analysis of local Embeddings
  • Scale up all local and referenced data into a Data-Lake

Tasks

Summarization

  • consolidate a long text into its main components
  • rephrase the content in an abstracted way

Sentiment analysis and classification

  • analyzing a list of posts to identify which ones were answered and which not
  • analyze a list of features or requirements to identify the type and cluster them in groups
  • Named Entity Recognition by identifying certain categories or tagging a special type

Content consolidation and linking

  • identify similarities and duplicates
  • identify contradictions
  • identify relationships between text elements
  • identify references between text elements

Question answering

  • search for relevant info
  • provide an answer to question with a reference

Example applications

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