1. Summarization for entity annotation Contextual summary
徐丹云

2. Experiments Introduction Background Approachs Problem Part One 1
Part Two 2
Part Three 3
Part Four 4
Part Five 5
Introduction
Background
Problem
Approachs
Experiments

3. Introduction Three Steps Identity Ranking disambiguation
Entity annotation（entity linking, entity disambiguation）
Three Steps
Identity
Ranking
disambiguation
Introduction
Figure[2]

4. Background System Classification Munual annotation
New York Times
Semi-automatic annotation
ZenCrowd[5]
Automatic annotation
AIDA[7]
DBpediaSpotlight[6]
…….
Background

5. Background Approaches： Machine learning Crowdsourcing General
Supervised
Semi-supervised
Crowdsourcing
Background

6. Motivation Have human in the loop of entity annotation(crowdsourcing…)
For example [5]
Descriptions are lengthy & inefficient verification
Annotate more efficiently
Guarantee accuracy
Motivation

7. Problem Automatically generate a summary (or summaries)
Input: entity mention & its surrounding text; candidate entity (or entities)
Output: summaries of candidate entity (or entities)
Problem

8. Approaches Features： Model Popularity of entity
Similarity between entity and text
Entity type
Name String Comparison ……
Model
Approaches

9. Experiments DataSet Ground truth Knowledge base
AQUAINT[8](
(50 documents : newswire stories from the Xinhua News Service, the New York , and the Associated Press; average of 9 links per document; link to wikipedia； wrong and right)
IITB[9](
(100 manually annotated texts, Web pages about sport, entertainment, science and technology, and health; annotations, 3800 distinct Wikipedia entities)
AIDA/CoNLL[2](
(newswire articles; YAGO2 entities) ……
Knowledge base
DBpedia
Experiments

10. Experiments Evaluation BaseLine Time Accuracy Running efficiency
[5]Total Description（corresponding webpage）
One VS multiple
Experiments

11. [1]Cornolti M, Ferragina P, Ciaramita M
[1]Cornolti M, Ferragina P, Ciaramita M. A framework for benchmarking entity-annotation systems[C]//Proceedings of the 22nd international conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2013:
[2] Hoffart J, Yosef M A, Bordino I, et al. Robust disambiguation of named entities in text[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011:
[3] Uren V, Cimiano P, Iria J, et al. Semantic annotation for knowledge management: Requirements and a survey of the state of the art[J]. Web Semantics: science, services and agents on the World Wide Web, 2006, 4(1):
[4] Shen W, Wang J, Han J. Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions[J].
[5] Demartini G, Difallah D E, Cudré-Mauroux P. ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking[C]//Proceedings of the 21st international conference on World Wide Web. ACM, 2012:
[6] Mendes P N, Jakob M, García-Silva A, et al. DBpedia spotlight: shedding light on the web of documents[C]//Proceedings of the 7th International Conference on Semantic Systems. ACM, 2011: 1-8.
[7] Yosef M A, Hoffart J, Bordino I, et al. Aida: An online tool for accurate disambiguation of named entities in text and tables[J]. Proceedings of the VLDB Endowment, 2011, 4(12):
[8] Milne D, Witten I H. Learning to link with wikipedia[C]//Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 2008:
[9] Kulkarni S, Singh A, Ramakrishnan G, et al. Collective annotation of Wikipedia entities in web text[C]//Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009:
references

12. Thank
You