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Linking named entities in Tweets with knowledge base via user interest modeling. Author: Chen Li Bin Wang Xiaochun Yang Speaker: Annan Wei. Outline. Introduction Tweet Entity Linking KAURI Framework Experiments Conclusion. Introduction. Twitter: a popular micro-blogging platform

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linking named entities in tweets with knowledge base via user interest modeling

Linking named entities in Tweets with knowledge base via user interest modeling

Author: Chen Li

Bin Wang

Xiaochun Yang

Speaker: Annan Wei

outline
Outline
  • Introduction
  • Tweet Entity Linking
  • KAURI Framework
  • Experiments
  • Conclusion
introduction
Introduction
  • Twitter: a popular micro-blogging platform

important information source

  • Tweets: users can publish and share information

topics ranging from daily life to news

events

Sun: the star at the center of the Solar System

Sun Microsystems: a multinationalcomputer company

Sun-HwaKwon : a fictional character

or many other entities named “Sun”.

outline1
Outline
  • Introduction
  • Tweet Entity Linking
  • KAURI Framework
  • Experiments
  • Conclusion
tweet entity linking
Tweet Entity Linking
  • Tweet Entity Linking:

The task to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge base.

  • Previous methods:
    • linking entities in Web documents
    • Context Similarity
    • Topical coherence
  • Challenging:

noisy ,short ,informal nature

tweet entity linking1
Tweet Entity Linking
  • Intra-tweet local information:
    • prior probability, similarity and topically coherent
  • Inter-tweet user interest information

input

output

t1->Bulls 1.Bulls(rugby) 2.Chicago Bulls 3.Bulls,New Zealand (not Work well)

t3->Scott (not Work well)

t2->Sun: 1. Sun 2.Sun Microsystems 3.Sun-Hwa Kwon (Work well)

outline2
Outline
  • Introduction
  • Tweet Entity Linking
  • KAURI Framework
    • Graph construction
    • Initial interest score estimation
    • User interest propagation algorithm
  • Experiments
  • Conclusion
kauri framework
KAURI Framework
  • Assumption 1.

Each Twitter user has an underlying topic interest distribution over various topics of named entities.

  • Assumption 2.

If some named entity is mentioned by a user in his tweet, that user is likely to be interested in this named entity.

  • Assumption 3.

If one named entity is highly topically related to the entities that a user is interested in, that user is likely to be interested in this named entity as well.

kauri framework1
KAURI Framework
  • Construct a graph of which the structure encodes the interdependence information between different named entities
  • Estimate the initial interest score for each named entity in the graph based on the intra-tweet local information
  • User Interest Propagation Algorithm, to propagate the user interest score among different named entities across tweets using the interdependence structure of the constructed graph
graph construction
Graph construction

G =(V, A, W)

Weight:

  • Indicating the strength of interdependence
  • Calculated using the Wikipedia Link-based Measure
initial interest score estimation
Initial interest score estimation

Initial interest score

Context similarity

Prior Probability

Topical coherence in tweet

For tweet t1 which lack intra-tweet context information to link entity mention”Bulls”.

For tweet t4,the prior probability candidate entity :

Tony Allen(musician) > Tony Allen(backetball),

But initial interest scores is higher than Tony Allen(musician).

α + β + γ = 1

user interest propagation algorithm
User interest propagation algorithm

The Final interest score

The interest propagation strength matrix

Initial interest score

outline3
Outline
  • Introduction
  • Tweet Entity Linking
  • KAURI Framework
  • Experiments
  • Conclusion
experiments
Experiments
  • Data set:
  • Tweet entity linking consists of detecting all the named entity mentions in all tweets and identifying their corresponding mapping entities exist in YAGO.
experiments1
Experiments

LOCALfull and KAURIfull: performance by leveraging all the intra-tweet local features.

LOCALβ=0,γ=0and KAURIβ=0,γ=0:

when we calculate the initial interest score using Formula 4, we set β=0and γ=0.

outline4
Outline
  • Introduction
  • Tweet Entity Linking
  • KAURI Framework
  • Experiments
  • Conclusion
conclusion
Conclusion
  • Proposed KAURI, a graph-based framework that combined intra-tweet local information with the inter-tweet user interest information.
  • KAURI achieves high performance in term of accuracy and efficiency ,and scales well to tweet stream.
slide19

Thanks!

Question?