> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/natural-language-processing-nlp/name-matching.md). # Name Matching Matching person names across two lists is fuzzy string matching with a harder twist: spelling, romanization, and culture all vary for the same identity. The page first reads how others solved it, then lists the public name datasets to test against, and ends with the libraries that do the matching. The same notes are in [String Matching](/natural-language-processing-nlp/string-matching.md). ## Articles The problem is best understood through people who had to solve it. [Analytics Vidhya](https://medium.com/analytics-vidhya/fuzzy-name-matching-datasets-1ae28884f226) on fuzzy name matching datasets, by Zaki Jefferson, frames it across datasets. [Fuzzy matching people names](https://medium.com/data-science/fuzzy-matching-people-names-6e738d6b8fe) starts from a concrete case: given two unordered lists with real people names, match identities in between. Name Matching Across datasets — POC by Centere of Excellence in AI National Informatics Centre was the government proof of concept; its address no longer opens and is kept at the end of the page. For the algorithms themselves, [fuzzy name matching algorithms](https://medium.com/data-science/python-tutorial-fuzzy-name-matching-algorithms-7a6f43322cc5) is Felix Kuestahler's Python tutorial, the fifth article in his series on Python data exploration. ## Datasets An algorithm is only as convincing as the names it was tested on. The largest is the [first and last name dataset](https://github.com/philipperemy/name-dataset), philipperemy/name-dataset, the Python library for names built from facebook 533M records, by philippe remy. The data.world open data community keeps [data.world name datasets](https://data.world/datasets/names), and FiveThirtyEight's Most Common Name Dataset is on [Kaggle](https://www.kaggle.com/datasets/fivethirtyeight/fivethirtyeight-most-common-name-dataset).

Kaggle name datasets by fivethirtyeight.

The figure shows those FiveThirtyEight name datasets as they appear on Kaggle. When gender is the attribute to predict or control for, the UCI Machine Learning Repository has the [gender by name dataset](https://archive.ics.uci.edu/ml/datasets/Gender+by+Name). For evaluation across cultures, the [paper](http://www.lrec-conf.org/proceedings/lrec2008/pdf/291_paper.pdf) — a ground truth dataset for matching coltural diverse romanized person names. ## Tools With articles and data in hand, the remaining piece is a library that matches at scale. [Dedupe](https://www.reddit.com/r/datasets/comments/4zrozk/request_name_matching_dataset/) — a python library for accurate and scalable fuzzy matching record deduplication and entity resolution. [name](https://github.com/bradhackinen/nama) — fast flexible name matching for large datasets. The athenianco repository is the [name matcher](https://github.com/athenianco/names-matcher) shown below.

To test any of these tools, the Kaggle, Name datasets, by fivethirtyeight are also linked at a fixed version: ## Deprecated links {% hint style="warning" %} These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above. {% endhint %} * Fuzzy matching people names by vadim markovtsev. This address no longer opens: * Name Matching Across datasets - POC by Centere of Excellence in AI National Informatics Centre. This address no longer opens: * fuzzy name matching algorithms by felix kuestahler. This address no longer opens: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/natural-language-processing-nlp/name-matching.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.