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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 23 6

  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 6 3

  3. Resources Resources Public

    Sample Memos (.csv and .py file)

    Python 1

  4. About About Public

    Information about our Project

Repositories

Showing 4 of 4 repositories
  • 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.

    Pilot-NER/Natural-Language-Processing-Name-Entity-Extraction’s past year of commit activity
    Python 6 MIT 3 0 0 Updated Apr 29, 2018
  • Resources Public

    Sample Memos (.csv and .py file)

    Pilot-NER/Resources’s past year of commit activity
    Python 1 0 0 0 Updated Apr 22, 2018
  • About Public

    Information about our Project

    Pilot-NER/About’s past year of commit activity
    0 0 0 0 Updated Apr 22, 2018
  • Rule-based-Named-Entity-Recognition Public

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

    Pilot-NER/Rule-based-Named-Entity-Recognition’s past year of commit activity
    Python 23 MIT 6 0 0 Updated Apr 21, 2018

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