  Name matching is a key component of systems for entity resolution or record
linkage. Alternative spellings of the same names are a com- mon occurrence in
many applications. We use the largest collection of genealogy person records in
the world together with user search query logs to build name matching models.
The procedure for building a crowd-sourced training set is outlined together
with the presentation of our method. We cast the problem of learning
alternative spellings as a machine translation problem at the character level.
We use in- formation retrieval evaluation methodology to show that this method
substantially outperforms on our data a number of standard well known phonetic
and string similarity methods in terms of precision and re- call. Additionally,
we rigorously compare the performance of standard methods when compared with
each other. Our result can lead to a significant practical impact in entity
resolution applications.
