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.

1. Recruiting SolutionsRecruiting SolutionsRecruiting Solutions
Daniel Tunkelang
Head, Query Understanding
better search through
query understanding

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:
2

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
3

4. Information need query select from results
rank using IR model
user:
system:
tf-idf PageRank
query understanding
4

5. search is a communication problem
5

6. 6
tag: skill OR title
related skills:
search, ranking, …
tag: company
id: 1337
industry: internet
verticals:
people, jobs
intent: exploratory

7. query understanding pipeline
7
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured query
+
annotations

8. query understanding pipeline
8
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured query
+
annotations

9. 9
fix obvious typos
help users spell names
spelling correction

10. spelling out the details
10
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
11
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
12
problem: corpus & query logs contain spelling errors
solution: use query chains to infer correct spelling
[product manger] [product manager] CLICK
[marissa mayer] CLICK

13. query understanding pipeline
13
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured query
+
annotations

14. query tagging: identifying entities in the query
14
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 )

15. query tagging: identifying entities in the query
15
TITLE CO GEO
MORE PRECISE MATCHING WITH DOCUMENTS

16. entity-based filtering
16
BEFORE

17. entity-based filtering
17
AFTER
BEFORE

18. entity-based filtering
18
BEFORE

19. entity-based filtering
19
AFTER
BEFORE

20. entity-based suggestions
20

21. entity-based suggestions
21

22. query tagging: sequential model
22
EMISSION PROBABILITIES
(learned from user profiles)
TRANSITION PROBABILITIES
(learned from query logs)
TRAINING

23. query tagging: sequential model
23
INFERENCE
given a query, find the most likely sequence of tags

24. query understanding pipeline
24
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured query
+
annotations

25. vertical intent prediction: distribution
25
JOBS
PEOPLE
COMPANIES
(probability distribution over verticals)

26. vertical intent prediction: relevance
26
[company]
[employees]
[jobs]
[name search]

27. query understanding pipeline
27
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured query
+
annotations

28. 28
query expansion: name synonyms

29. 29
query expansion: job title synonyms

30. 30
query expansion: signals
[jon] [jonathan] CLICK
trained using query chains:
[programmer] [developer] CLICK
symmetric but not transitive!
[francis] ⇔ [frank]
[franklin] ⇔ [frank]
[francis] ≠ [franklin]
[software engineer] [software developer] CLICK
context based!
[software engineer] => [software developer]
[civil engineer] ≠ [civil developer]

31. query understanding pipeline
31
spellcheck
query tagging
vertical intent prediction
query expansion
raw query
structured query
+
annotations

32. 32
what else can we learn from search in the wild?

33. don’t guess when it’s better to ask
33
vs.

34. clarify then refine
34
computers books

35. give users transparency, guidance, and control
35

36. think beyond individual search queries
36
Gene Golovchinsky, FXPAL

37. know when you don’t know
37
Claudia Hauff, Query Difficulty for Digital Libraries [2009]

38. 38
Daniel Tunkelang
dtunkelang@linkedin.com
https://linkedin.com/in/dtunkelang