improve main readme
![image](https://user-images.githubusercontent.com/7570107/85285114-3969ac00-b488-11ea-88ff-5fca1b34af1f.png)
-`splink` is a Python package for probabilistic record linkage (entity resolution), within the Fellegi-Sunter framework.
+`splink` is a Python package for probabilistic record linkage (entity resolution).
-It's key features are:
+Its key features are:
- It is extremely fast. It is capable of linking a million records on a laptop in around a minute.
- It is highly accurate, with support for term frequency adjustments, and sophisticated fuzzy matching logic.
-- It supports multiple SQL backends, meaning it's capable of running at any scale. For smaller linkages of up to a few million records, no additional infrastructure is needed. For larger linkages, Splink currently supports Apache Spark or AWS Athena as backends.
+- It supports running linkage against multiple SQL backends, meaning it's capable of running at any scale. For smaller linkages of up to a few million records, no additional infrastructure is needed . For larger linkages, Splink currently supports Apache Spark or AWS Athena as backends.
- It produces a wide variety of interactive outputs, helping users to understand their model and diagnose linkage problems.
+The core linkage algorithm is an implementation of Fellegi-Sunter's canonical model of record linkage, with various customisations to improve accuracy. Splink includes an implementation of the Expectation Maximisation algorithm, meaning that record linkage can be performed using an unsupervised approch (i.e. labelled training data is not needed).
+
## Acknowledgements
We are very grateful to [ADR UK](https://www.adruk.org/) (Administrative Data Research UK) for providing funding for this work as part of the [Data First](https://www.adruk.org/our-work/browse-all-projects/data-first-harnessing-the-potential-of-linked-administrative-data-for-the-justice-system-169/) project.
