This document discusses query understanding in search engines. It describes how query understanding involves identifying entities and tags in queries, predicting the user's intent or topic area, expanding queries using related terms, and incorporating spelling corrections. The key aspects of query understanding covered are tagging queries for entities like names, titles, companies; predicting the user's vertical intent like jobs, people or companies; and expanding queries using name synonyms, job title synonyms or signals from past user queries and clicks. The document also suggests giving users more transparency, guidance and control over the search process.
2. overview
query understanding: what is it?
how we do query understanding at LinkedIn
some other thoughts from search in the wild
what I’m not going to cover:
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3. Information need query select from results
rank using IR model
user:
system:
tf-idf PageRank
bird’s-eye view of how a search engine works
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4. Information need query select from results
rank using IR model
user:
system:
tf-idf PageRank
query understanding
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10. spelling out the details
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PEOPLE NAMES
COMPANIES
TITLES
PAST QUERIES
n-grams
marissa => ma ar ri is ss sa
metaphone
mark/marc => MRK
co-occurrence counts
marissa:mayer = 1000
marisa meyer yahoo
marissa
marisa
meyer
mayer
yahoo
11. spelling out the details
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problem: corpus as well as query logs contain many spelling errors
certain spelling errors are quite frequent
while genuine words (especially names) might be infrequent
12. spelling out the details
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problem: corpus & query logs contain spelling errors
solution: use query chains to infer correct spelling
[product manger] [product manager] CLICK
[marissa mayer] CLICK
14. query tagging: identifying entities in the query
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TITLE CO GEO
TITLE-237
software engineer
software developer
programmer
…
CO-1441
Google Inc.
Industry: Internet
GEO-7583
Country: US
Lat: 42.3482 N
Long: 75.1890 W
(RECOGNIZED TAGS: NAME, TITLE, COMPANY, SCHOOL, GEO, SKILL )