Presentation from Prof. Dr. Maarten de Rijke, Professor of Information Retrieval at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam.
Many information needs concern people. Effective retrieval methods for people search require effective representation, discovery and presentation methods. In this presentation Maarten de Rijke surveys recent entity retrieval methods, recent methods for discovering aspects of entities, and emerging ideas to blend the outcomes of such methods as part of mixed initiative conversational scenarios, where the search engine does not just answer questions but explores options and even generates questions to help a searcher improve their effectiveness.
Handwritten Text Recognition for manuscripts and early printed texts
Mixed Initiative Search - Prof. Dr. Maarten de Rijke
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Mixed initiative search
Maarten de Rijke
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• Based on joint work with David Graus, Evangelos Kanoulas, Edgar
Meij, Daan Odijk, Ridho Reinanda, Manos Tsagkias, Christophe Van
Gysel, Nikos Voskarides, Wouter Weerkamp, Marcel Worring, Masrour
Zoghi
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It’s all about entities
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• Entities (people, locations, organizations, …) play central organizing
role
• In search
• in web search, up to 70% of the queries are entity queries (Lin et
al., 2012; Guo et al., 2011)
• in academic search, the proportion of queries that contain entities
is over 93% (Li et al., 2016)
• increasingly, entities are retrievable items
It’s all about entities
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search
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search
mothers
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Where are we now?
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• Diverse intents
• You know an entity by the stuff it hangs out with
• Words
• Facets
• Entities
• Organizations relations
• …
Information needs around entities
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• Mine search logs to discover aspects
• Prioritizing entity display
• Providing direct action
• “Query-less” entity-oriented
diversification
• Supporting complex search tasks
• Knowledge base design/construction
Discovering entity aspects
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• Automatically generate human-readable
explanations of related entities
• Mine large volumes of text: fragments in which
entities co-occur
Entity relations
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• Update description of long tail
entity with information from range
of sources
• Learn to adjust representations
based on clicks
• Enriching the representation with
additional descriptions helps
improve retrieval
• Continuously updating the ranker
helps
Entity updates
KBER
DCER
sim
sim
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• Unsupervised model construction,
efficient entity capabilities query, and
semantic matching between query
terms and candidate entities
• Learn mappings between words and
entities, as well as distributed
representations of words and entities
• Words that are strongly evidential for
particular products are projected
nearby those products
• Very effective, highly scalable
Unsupervised entity ranking
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What is next?
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document list
action at
query
state st
user
environment
examine
document list
generate implicit
feedback
reward rt
implicit
feedback
evaluation
measureretrieval system
agentagent
action at
environment
reward rt
state st
Image taken from K. Hofmann, S. Whiteson, and M. de Rijke. Balancing exploration and exploitation in online learning to rank. In ECIR 2011, April 2011.
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• Go beyond the traditional search engine result page
• What should the search engine say?
• When should it switch?
With all of that information around an entity
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• Old-fashioned search engine result page (SERP)?
• Direct answer?
• Engage in a conversation?
• Generate a news article?
• Produce a timeline?
• Multi-document summary?
• Wikipedia page?
What should a search engine say?
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• Working with media professionals in an audiovisual archive
• It is their business to create narratives
• Wrapping extensive interviewing rounds with media professionals
• Next
• Annotate and mine semantic aspects of narratives media professionals
create
• Learning templates for SERP generation
• Learning to re-order elements on the SERP
• Generating natural language to connect the elements
Example: narrative search
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Example: Engage in a conversation
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• Run an A/B test
• Explore online
• Learn from historical data (“counterfactual reasoning”)
When should should it switch?
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Would people buy it?
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document list
action at
query
state st
user
environment
examine
document list
generate implicit
feedback
reward rt
implicit
feedback
evaluation
measureretrieval system
agentagent
action at
environment
reward rt
state st
Based on K. Hofmann, S. Whiteson, and M. de Rijke. Balancing exploration and exploitation in online learning to rank. In ECIR 2011, April 2011.
answer format
+
answers
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