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Friday, April 24 • 3:20pm - 3:40pm
Statistical Machine Translation Approach for Name Matching in Record Link

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Record linkage, or entity resolution, is an important area of data mining. Name matching is a key component of systems for record linkage. Alternative spellings of the same name are a common 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 information 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 recall. Our result can lead to a significant practical impact in entity resolution applications.


Jeffrey Sukhare

BS, MS Computer Science UC Santa Cruz, PhD candidate Computer Science UC Davis.  Senior Data Scientist at Ancestry.com working on record linkage applications. 

Friday April 24, 2015 3:20pm - 3:40pm

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