Intent detection and Slot filling
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Updated
Jan 7, 2020 - Python
Entity resolution (also known as data matching, data linkage, record linkage, and many other terms) is the task of finding entities in a dataset that refer to the same entity across different data sources (e.g., data files, books, websites, and databases). Entity resolution is necessary when joining different data sets based on entities that may or may not share a common identifier (e.g., database key, URI, National identification number), which may be due to differences in record shape, storage location, or curator style or preference.
Intent detection and Slot filling
Tool to explain Entity Resolution model predictions
utilities for working with Entity Resolution models
🔎 Finds fuzzy matches between datasets
Entity Resolution and Record Linkage library
TFIDF / KNN based string matching
Super Fast String Matching in Python
🗃️ Small library to simplify collecting and loading of entity alignment benchmark datasets
Submitted solution for the ACM SIGMOD 2022 Programming Contest 💻🏅
🕸️ Little helper for handling entity clusters
A maximum-strength name parser for record linkage.
Company Match algorithm with Spark and Python on DataBricks
Deduplicate data using fuzzy and deterministic matching rules.
🆔 A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
Code and data for the paper: Towards Universal Dense Blocking for Entity Resolution
Created by Halbert L. Dunn
Released 1946