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Question Answering With Elasticsearch
Question Answering With Elasticsearch When people hear Elasticsearch, there is a natural inclination to just think about search. While search is a major focus, it is not just about search. At its core, Elasticsearch is designed to help you converse with your data and tell stories with it. Search capabilities allow you to filter data down to the information that is relevant to the questions you want to ask, but it is the other features that allow you to get more in depth when your data is modeled appropriately. These features help you ask questions of your data and can expose intelligence and actionable insights in support of your core mission. In this session, we will walk through some powerful ways that you can use Elasticsearch for question answering and discuss how ClearQuery can make that even easier. Tim Tutt has over a decade of software engineering experience with 8 years specifically developing and deploying large scale search and discovery and data analytics solutions. Tim has experience in deploying and supporting production applications and building ad-hoc solutions and analytics in support of various customer missions. He is a proven technical leader and experienced technologist with the ability to quickly derive value in unique ways by thinking outside of the box to deliver solutions for customers in the public and private sectors. When he's not coding away, you might find him at a poker table. Elasticsearch Solutions at FiscalNote We will give an overview on the many ways FiscalNote uses Elasticsearch. From percolators to discovery documents based on client queries, applying topic models, multi-lingual text search, optimized cluster configuration using SSD data nodes to determining lexical similarity of federal regulatory comments. Andrew Hian-Cheong has been an engineer at FiscalNote for the past 3 years. Joining after he graduated Georgetown University, where he studied both Computer Science and Government, he has worked on a range of projects using Elasticsearch from setting up a centralized logging and monitoring ELK stack to building a new document discovery system using the percolators feature. Currently, Andrew is working on implementing a topics hierarchy tagging system using Elasticsearch as a Machine Learning Engineer while pursuing a master at University of Maryland, College Park. If you’re interested in Elastic trainings [there is a fee], our team is coming to Washington DC on October 22-24. You can register here:

Fiscal Note

1201 Pennsylvania Ave NW Floor 6 · Washington, dc


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This group is for anyone interested in open source search, logging, analytics & data visualization. We primarily focus on these open source projects — Elasticsearch, Kibana, Logstash, and Beats. We welcome talks on any aspect of data exploration and information retrieval that somehow involves the Elastic stack.

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