The document discusses the implementation and design of an autocomplete feature in LexisNexis, aimed at improving search relevance for legal and professional information users. It covers aspects such as data preparation, the use of Solr for fast lookup, and strategies for handling user queries effectively, including suggestions for spelling correction and duplicate suggestions. The presentation concludes with a roadmap for future developments and invites questions from the audience.

1. Autocomplete as Relevancy
Haystack Search Relevance Conference
April 24, 2019
Rimple Shah
Revanth Malay
David Rhodes

2. 2
LexisNexis
 Business – Information for Lawyers and other Professionals
 Mission: Advance the Rule of Law
 Flagship Products: Lexis Advance, Lexis Risk Solutions, Nexis
 Target Markets: Legal, Risk, Government, Academia, Professional Information
Users
 Customers in 130 countries
 Subsidiary of RELX (NYSE: RELX) since 1994
 Primary Direct Competitors: Dow Jones, Thomson Reuters, Wolters Kluwer,
Bloomberg
 > 10,000 employees worldwide

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Agenda
 Autocomplete as Search Relevance
 Use case
 Customizing Solr for Autocomplete
 Architecture Design
 Evaluating Relevance
 Future Roadmap

4. 4
Autocomplete is Search Relevance

5. 5
Autocomplete in Lexis Advance

6. 6
Who’s Your Data?
 The suggestion is the document
<doc>
<field name="query">obamacare</field>
<field name="display">patient protection and affordable care act</field>
<field name="token_count">1</field>
<field name="id">urn:label:7AC7C01A1EF546C4BCDF334557</field>
<field name="popularity">8464</field>
<field name="source">KRM</field>
<field name="region">United States</field>
</doc>

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Where Do Suggestions Come From?
Users’ Queries LexisNexis legal experts

8. 8
Data Preparation

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Solr Suggester
 Built-in Solr Search Component
 Features
 Fast in-memory Finite State Transducer data structure
 Easy to add to an existing Solr index

10. 10
• “index + query” approach for complex weights calculation, stop
words removal, or basic context filtrationFunctionality
• Lookups against in-memory FST work incredibly fast
• Performance of a well-tuned Solr Index is sufficient for this use
case
Performance
Should We Use Solr Suggester?

11. 11
Basic Solr Configuration
 Keyword Tokenizer
 Lowercase Filter Factory
 EdgeNGram Filter Factory
 MinGramSize=3
 MaxGramSize=30
Motion to Dismiss
 mot
 moti
 motio
 motion
 motion t
 motion to
 motion to di
 motion to dis
 …
 Whitespace Tokenizer
 Lowercase Filter Factory
 EdgeNGram Filter Factory
 MinGramSize=1
 MaxGramSize=30
Motion to Dismiss
 m t d
 mo to di
 mot dis
 moti dism
 motio dismi
 motion dismis
 dismiss
 Whitespace Tokenizer
 Lowercase Filter Factory
Motion to Dismiss
 motion
 to
 dismiss

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Term Frequency
motion to dis
T.F. = 1.0
motion to dismiss
plaintiff’s motion
motion to dismiss
Solution
Problem
motion to dis

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EDisMax's pf2 Parameter
• Boost suggestions that have user query tokens next to each other.
• Example:
User Query: plaintiff’s rebuttal expert witness
Suggestions:
Doc1 : rebuttal expert witness | Score: 292
Doc2 : rebuttal witness and expert testimony | Score: 253

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Preference for First Word Match
Insert an anchor term as the first token in index and query time.
Example :
User query : motion dismiss KXQHZ motion dismiss
Suggestions:
Documents Index
motion to dismiss with prejudice KXQHZ motion to dismiss with prejudice
dismiss motion with prejudice KXQHZ dismiss motion with prejudice

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Incorrectly Matching On Partial Words
• Query suggestion incorrectly considers complete token as partial word and provides token
suggestions that start with the word.
User Query Documents Index
government is a virgin islands government act v i g a
vi is go ac
vir isl gov act
virg isla gove
virgi islan gover
virgin island govern
…….
government

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Correctly Matching On Partial Words
• Condition 1: When user query has no trailing space
•Insert ‘xwkq’ in the beginning of the last token
User Query Documents Index
government is a
xwkq
virgin islands government act xwkqv xwkqi xwkqg xwkqa
xwkqvi xwkqis xwkqgo xwkqac
xwkqvir xwkqgov xwkqact
xwkqvirg xwkqgove
xwkqvirgi xwkqgover
xwkqvirgin xwkqgovern
…….
xwkqgovernment

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Correctly Matching On Partial Words
• Condition 2: When user query has trailing space
• Rest of the Solr analyzers do the job here
User Query Documents Index
government is a_ virgin islands government act xwkqv xwkqi xwkqg xwkqa
xwkqvi xwkqis xwkqgo xwkqac
xwkqvir xwkqgov xwkqact
xwkqvirg xwkqgove
xwkqvirgi xwkqgover
xwkqvirgin xwkqgovern
…….
xwkqgovernment

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Exact Token Match Before Stemmed & Synonym Match
^8
^6
^6
Standard Tokenizer Factory
+
Lowercase Filter Factory
Standard Tokenizer Factory
+
Lowercase Filter Factory
+
Snowball Porter Filter Factory
+
English Possessive Filter Factory
Standard Tokenizer Factory
+
Lowercase Filter Factory
+
Synonym Graph Filter Factory

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Duplicate and Near Duplicate Suggestions
• Reduce the impression of repetitive suggestion by reduce the suggestion word from the same
root
• User Query: zoning var
• Suggestions:

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Reduce Near Duplicate Suggestions
zone variance
zoning variance
zone variances
variance of zoning

variance_zone

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• About 10-12 % of user queries to web search
engines have spelling errors
Spelling Correction

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Spelling Correction

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Architecture Design

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Architecture Design

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Offline Evaluation

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Offline Evaluation Feedback

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Online Evaluation
• Measure user engagement with
autocomplete suggestions
• Click-rate
• Mean Reciprocal Rank (MRR)
• Minimum Keystroke (MKS)

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Future Roadmap

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Any Questions ?

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Thank You
• Rimple Shah
• rimple.shah@lexisnexis.com
• Revanth Malay
• revanth.malay@lexisnexis.com
• David Rhodes
• david.rhodes@lexisnexis.com
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