From 6c500d37b948a586a1a543733f7603353aee2a55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?St=C3=A9phane=20Collot?= Date: Mon, 18 May 2026 19:42:41 +0100 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 587bad7..41a0a18 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ Entity Matching Model (EMM) solves the problem of matching company names between two possibly very large datasets. EMM can match millions against millions of names with a distributed approach. It uses the well-established candidate selection techniques in string matching, -namely: tfidf vectorization combined with cosine similarity (with significant optimization), +namely: tfidf vectorization combined with cosine similarity (with significant optimization, in part thanks to [sparse_dot_topn](https://github.com/ing-bank/sparse_dot_topn)), both word-based and character-based, and sorted neighbourhood indexing. These so-called indexers act complementary for selecting realistic name-pair candidates. On top of the indexers, EMM has a classifier with optimized string-based, rank-based, and legal-entity @@ -141,4 +141,4 @@ Please note that INGA-WB provides support only on a best-effort basis. ## License -Copyright ING WBAA 2023. Entity Matching Model is completely free, open-source and licensed under the [MIT license](https://en.wikipedia.org/wiki/MIT_License). +Copyright ING WBAA 2026. Entity Matching Model is completely free, open-source and licensed under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).