fuzzup 0.1.2

A Fuzzy Matching Approach for Clustering Strings

Fuzz Up [W.I.P.]

fuzzup offers (1) a simple approach for clustering string entitities based on Levenshtein Distance using Fuzzy Matching in conjunction with a simple rule-based clustering method.

fuzzup also provides (2) functions for computing the prominence of the resulting entity clusters resulting from (1).

fuzzup has been designed to fit the output from NER predictions from the Hugging Face transformers NER pipeline specifically.

Installation guide

fuzzup can be installed from the Python Package Index (PyPI) by:

pip install fuzzup

If you want the development version then install directly from Github.

Workflow

... COMING SOON!

To do

• document whitelist matching in showcase
• update readme with workflow
• tests for whitelist
• try and tune on junges entitites
• cutoff_threshold -> score_cutoff -> cdist
• run against tores list
• document whitelist
• update docs

Background

fuzzup is developed as a part of Ekstra Bladet’s activities on Platform Intelligence in News (PIN). PIN is an industrial research project that is carried out in collaboration between the Technical University of Denmark, University of Copenhagen and Copenhagen Business School with funding from Innovation Fund Denmark. The project runs from 2020-2023 and develops recommender systems and natural language processing systems geared for news publishing, some of which are open sourced like fuzzup.

Read more

The detailed documentation and motivation for fuzzup including code references and extended workflow examples can be accessed here.

Contact

We hope, that you will find fuzzup useful.

Please direct any questions and feedbacks to us!

If you want to contribute (which we encourage you to), open a PR.

If you encounter a bug or want to suggest an enhancement, please open an issue.