Elasticsearch Goes to Congress
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Learn how Congress is starting to use Elasticsearch to modernize search, and how you can help to accelerate this change. Until just a decade ago, some of the most important precedents of Congress were stored and indexed on paper cards. This system was replaced by a slow, clunky SQL search and there was initially resistance when Xcential proposed to convert this to Elasticsearch (v1.7.2), because lawyers feared that ‘relevance’-based search would be less precise. With accurate results, 50 times faster, users were won over. Now there is a potential to use modern search, not only for precedents, but throughout the work of Congress and in state legislatures as well. We’ll talk about some of these projects, and how you could get involved (we’re hiring!).
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Abstract:
In this talk, Jeff will talk about how he designed a command-line tool built on the go-elasticsearch project - making interactions with the elasticsearch APIs much easier. He will also go into the inspirations of the project, lessons learned, and future work.
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Elasticsearch Goes to Congress
- 1. Elasticsearch Goes to Congress Indexing law 1 ari@xcential.com xcential.com @arihersh Ari Hershowitz
- 2. 2 Who am I? Ari Hershowitz • Neuroscience, environmental advocacy, etc (prehistory - 2007) • Lawyer (Georgetown Law 2007) • Legal technology (2008 - Now) • Dad ari@xcential.com @arihersh github.com/aih/FlatGov blog.linkedlegislation.com
- 3. 3 My goals today 1. Show how we’re using search in law. 2. Get you to help us do it better.
- 4. 4 Overview • A brief history of legal ‘search’ • Unique challenges of search for law • Our work in Congress and beyond • A vision for modern legal search
- 5. 5
- 8. 8 Incremental indexing 1800’s Shepard’s citator printed gummed paper to add to law books to track new references to prior law
- 9. 9
- 10. 10 Legal search • Lawyers hate relevancy Complete >>> sort order Precision >>> efficiency • Lawyers hate stop-words: every ‘and’ is sacred • Legal researchers love booleans
- 11. 11
- 12. Our work 12 Office of the House Parliamentarian •Cards listing daily precedents for more than 200 years •Thousands of precedents •About 1 Gb of data Index cards -> SQL -> Elasticsearch
- 13. Our work 13 Bill similarity •Open source •“Plagiarism” software for bills in Congress •Using Elasticsearch/Lucene ‘more like this’
- 14. Bill similarity: one section 14 SEC. 2. IMPROVING ACCESS TO INFLUENTIAL VISITOR ACCESS RECORDS. (a) Definitions.—In this section: (1) COVERED LOCATION.—The term “covered location” means— (A) the White House; (B) the residence of the Vice President; and (C) any other location at which the President or the Vice President regularly conducts official business. (2) COVERED RECORDS.—The term “covered records” means information relating to a visit at a covered location, which shall include— (A) the name of each visitor at the covered location;
- 15. Bill similarity: many sections 15 Bill 116hr1500ih Sections 1 100 101 => similar sections in other bills 102 200 … Many ways to calculate cumulative similarity.
- 16. Our work 16 State law •Ingest XML •Link to sources •Translate user query to ES DSL •Create a user interface for legal search
- 17. Summary • Law is a natural fit for indexing, search and similarity metrics • Small data (1 GB) is important, too • Stop working on logs and work with us on legislative text 17