1 / 19

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. Introduction Tweet Entity Linking KAURI Framework Experiments Conclusion. Introduction. Twitter: a popular micro-blogging platform

oneida
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Linking named entities in Tweets with knowledge base via user interest modeling Author: Chen Li Bin Wang Xiaochun Yang Speaker: Annan Wei

  2. Outline • Introduction • Tweet Entity Linking • KAURI Framework • Experiments • Conclusion

  3. 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”.

  4. Outline • Introduction • Tweet Entity Linking • KAURI Framework • Experiments • Conclusion

  5. 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

  6. 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)

  7. Outline • Introduction • Tweet Entity Linking • KAURI Framework • Graph construction • Initial interest score estimation • User interest propagation algorithm • Experiments • Conclusion

  8. 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.

  9. 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

  10. Graph construction G =(V, A, W) Weight: • Indicating the strength of interdependence • Calculated using the Wikipedia Link-based Measure

  11. 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

  12. User interest propagation algorithm The Final interest score The interest propagation strength matrix Initial interest score

  13. Outline • Introduction • Tweet Entity Linking • KAURI Framework • Experiments • Conclusion

  14. 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.

  15. 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.

  16. Experiments

  17. Outline • Introduction • Tweet Entity Linking • KAURI Framework • Experiments • Conclusion

  18. 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.

  19. Thanks! Question?

More Related