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@Pilot-NER

Named-Entity Recognition for Transaction Memos

We implemented rule-based and machine-learning approaches to extract the vendors' names and locations from bank transaction memos with an accuracy of 89%.

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  1. Rule-based-Named-Entity-Recognition Rule-based-Named-Entity-Recognition Public

    We reached an accuracy of 75% in extracting vendors' names from transaction memos based on human-identified patterns

    Python 22 7

  2. Natural-Language-Processing-Name-Entity-Extraction Natural-Language-Processing-Name-Entity-Extraction Public

    Using Conditional Random Field (CRF) model for Named-Entity Recognition. Achieve 89% accuracy in extracting vendors' names and locations from bank memos provided by Pilot, Inc.

    Python 5 3

  3. Resources Resources Public

    Sample Memos (.csv and .py file)

    Python 1

  4. About About Public

    Information about our Project

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