Framing NER task as a text classification task

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Hi @nix411,

my small contribute to your problem. I would try to proceed like this:

  1. train a TEXTCAT to detect correctly sentences like "Revenue grew 10% to EUR 5 billion a rise from previous year where the revenue amounted to EUR 4 billion." where you know there is the information you need.
  2. train a NER to detect revenue enties like: "EUR 5 billion" , "EUR 4 billion".
  3. Use dependecy parsing to frame the meaning of your revenue entities, may be chaining it with other NER models to detect class of entities that are important (e.g. "previuos year")

look also here for a similar case: Ambiguity in NE tagging