Elasticsearch DC Meetup: Relevance Tuning with Genetic Algorithms

Details

Details:

Join us on December 10th from 6:00 PM- 8:00 PM at the Ballston Techspace for snacks, drinks, and a presentation from Tim Allison- "Relevance Tuning with Genetic Algorithms".

Abstract:

Relevance Tuning with Genetic Algorithms

This talk builds on work by Simon Hughes and others to apply genetic algorithms (GA) and random search for finding optimal parameters for relevance ranking. While manual tuning can be useful, the parameter space is too vast to be confident that one has found optimal parameters without overfitting. We'll present Quaerite (https://github.com/mitre/quaerite), an open source toolkit that allows users to specify experiment parameters and then run a random search and/or a GA to identify the best settings given ground truth. We'll offer an overview of mapping the Elastic parameter space to a GA problem, the importance of the baked-in n-fold cross-validation, and the surprises and successes found with deployed search systems.

Speaker Bio:

Tim Allison
Founder/Chief Architect, Rhapsode Consulting LLC

Tim has been working in natural language processing since 2002. In the
last 5+ years, his focus has shifted to content/metadata extraction
(and evaluation), advanced search and relevance tuning. Tim is a
member of the Apache Software Foundation (ASF), the chair/VP of Apache Tika and a committer on Apache POI (2013), Apache PDFBox (2016), and Apache Lucene/Solr (2018). Tim holds a Ph.D. in Classical Studies, and in a former life, he was a professor of Latin and Greek.