Dependency Network Based Real-time Query Expansion
Jiaqi Zou, Xiaojie WangCenter for Intelligence Science and Technology,
BUPT
Outline
• Introduction– What is RTQE?– Benefits of RTQE– Related Research– Improvements in our work
• Method– Query Intention– Dependency Relation Network– RTQE Method
Outline
• Experiments– Test of operation numbers– Test of expansion success percentage– Test of retrieval performance– Comparison with Bing
• Conclusion
Introduction- What is RTQE?
• RTQE is a kind of query expansion.• RTQE methods expand queries at
the same time when users type queries into the search box.
Introduction- Benefits of RTQE
• RTQE reduces user’s keystrokes and time to perform a query, especially useful for mobile device users.
• RTQE improves the query quality.
Related Research
• Most widely used method: string matching method using query log.
• Little work on RTQE takes query intention into account.– Strohmaier et al. suggested that explicit
queries containing at least one verb word might reflect possible user intentions.
Improvements in our work
• Represent query intention better.• Construct a RTQE method which
expands components of possible user query intentions.
• This RTQE method improves the retrieval performance.
Query Intention
• Task-oriented classification of query intention: – Navigational– Informational– Transactional
• Duan et al. suggested dependency related verb-noun pairs are good representation of informational and transactional query intentions.
Query Intention
• Verb-noun pair is not sufficient to represent query intention, other parts like attributes of noun are also very important.
• New representation:Verb-Attributes-Noun
• Example: buy new car tire, cook Chinese food
Dependency Relation Network
• To do query intention related RTQE, we built a dependency relation network which is a collection of numbers of query intentions.
• Steps:– Do dependency parsing on large corpus.– Extract all the verb-attributes-noun
structures. – Combine these structures to be the Network.
Dependency Relation Network
• Example : How to change a car tire• Extracted : change car tire
RTQE method
RTQE Example
Experiments
• Corpus: www.ehow.com – 915,000 articles– 20 categories(Health, Cars,
Food&Drink, etc)
Test of operation numbers
• Keystrokes and mouse clicks needed to generate a query is recorded. Each keystroke or mouse click is recorded as an operation.
1
1
( )n
x xx
n
xx
OpFull OpExpandedSaved
OpFull
Test of operation numbers
• Average saved operations is 63.75% after RTQE
Average number of operations
Without RTQE With RTQE
15.0 5.437
Test of expansion success percentage
• For a given query intention, if the user can find a query exactly related to this intention from the expanded list, we call it a successful expansion.
SuccessNumSuccessPercentage
AllNum
Test of expansion success percentage
Times
Query expansion success
Query expansion fail
168 32
• Expansion success percentage is 84%.
Test of retrieval performance
• We compare the retrieval performance of the three: – original query user typed in– the query after verb-noun expansion– the query after verb-attributes-noun
expansion.
• We use precision and nDCG score for evaluation.
Test of retrieval performance
Query Type Precision nDCG score
Original query word 0.73% 13.11%
Query after verb-noun expansion
9.47% 37.37%
Query after verb-attributes-noun expansion
79.2% 88.95%
Comparison with Bing
• The RTQE result of Bing differs a lot if the word order of a query changes.
Comparison with Bing
• Categories the RTQE result of Bing into 3 groups:– NOT: cannot get correct
recommendations – NORMAL: get correct recommendations
only in normal word order– ALL: can get correct recommendations
both in normal order and other word orders
Comparison with Bing
Group NOT NORMAL ALL
Percentage 49% 33% 18%
Conclusion
• Presented a novel RTQE method using a dependency relation network.
• This RTQE method is proved to be effective in representing user query intention and hence improve retrieval performance.
Thank you!
