1. Event Detection
Aliaksei Antonau 1
5. Juli 2016 1

2. Learning to Extract Local Events from the Web
John Foley, Michael Bendersky and Vanja Josifovski
Aliaksei Antonau
5. Juli 2016

3. Introduction
Aliaksei Antonau
5. Juli 2016

4. Introduction
Aliaksei Antonau
5. Juli 2016

5. Schema.org
“Schema.org is a collaborative, community activity with a mission to create, maintain, and promote schemas for structured data on the Internet, on web pages, in messages, and beyond.”
Aliaksei Antonau
5. Juli 2016

6. Schema.org
Aliaksei Antonau
5. Juli 2016

7. Event Extraction Model
Aliaksei Antonau
5. Juli 2016

8. Experimental Setup
Aliaksei Antonau
5. Juli 2016

9. Collecting Judgments
Aliaksei Antonau
5. Juli 2016

10. Event Prediction Results
Aliaksei Antonau
5. Juli 2016

11. Precision Evaluation
Aliaksei Antonau
5. Juli 2016

12. Aliaksei Antonau
5. Juli 2016

13. Aliaksei Antonau
5. Juli 2016

14. Conclusion
In this paper authors introduce task: to retrieve and recommend events that users might want to attend. They focus on the identification and extraction of local events.
Authors show, that using semantic web technologies, and specifically Schema.org microdata can be useful for training approaches to extraction problems like the one they explored.
Aliaksei Antonau
5. Juli 2016

15. 15
Aliaksei Antonau
5. Juli 2016

16. Generating Event Causality Hypotheses through Semantic Relations
Chikara Hashimoto, Kentaro Torisawa, Julien Kloetzer and Jong-Hoon Oh
Aliaksei Antonau
5. Juli 2016

17. Introduction
Goal: develop method of generating plausible event causality hypotheses from other event causalities extracted from the web.
Aliaksei Antonau
5. Juli 2016

18. Introduction Application Reason for generating NEW hypotheses:
Future event prediction
Why-question answering
Future scenario generation
Reason for generating NEW hypotheses:
It is unlikely that all the event causalities that we recognize in this world are written in corpora
Aliaksei Antonau
5. Juli 2016

19. Proposed Method
Semantic relation database records which binary pattern, e.g.,
A CAUSES B, which indicates a semantic relation, co-occurs
with which noun pairs.
Binary patterns:
Causation. A CAUSES B (deforestation and global warming)
Material. B IS MADE OF A (plutonium and atomic bomb)
Necessity. B REQUIRES A (ability to think and verbal aptitude)
Use. A IS USED FOR B ( er and exchanges of messages)
Prevention. A PREVENTS B (a mosquito net and malaria)
Aliaksei Antonau
5. Juli 2016

20. Hypotheses candidates generation
Proposed Method
Event Causalities,
extracted from the web
Extraction of phrase pairs as event causality candidates from single sentences in 600 million web pages.
Replacement of the original noun pair of a source event causality with other noun pairs from web.
Plausible hypothesis candidates are identified by an SVM classifier.
Hypotheses candidates generation
Hypotheses Ranking
Aliaksei Antonau
5. Juli 2016

21. Proposed Method
Generated event causality Deploy a mosquito net → avoid malaria On phrase pair level On noun pair level novelty hypotheses it novelty hypotheses it will be will be REJECTED IF Source includes: Deploy a → avoid Use a → prevent mosquito net malaria mosquito net malaria
Aliaksei Antonau
5. Juli 2016

22. Experimental Setup Source for extracting event causalities:
600 million web pages
132,528,706 event causality candidates extracted
2,451,254 event causalities after applying filters
These 2.4M events were used for generating hypotheses
All pages and events in Japanese language
Aliaksei Antonau
5. Juli 2016

23. Results in noun pair level novelty setting
Aliaksei Antonau
5. Juli 2016

24. Results in phrase pair level novelty setting
Aliaksei Antonau
5. Juli 2016

25. Conclusion
Authors proposed a method of hypothesizing plausible event causality hypotheses from event causalities extracted from the web by using semantic relations.
With 70% precision, proposed method generated 347,093 noun pair level novelty hypotheses and 302,350 phrase pair level novelty hypotheses from the 2.4M event causalities extracted from the web.
Aliaksei Antonau
5. Juli 2016

26. Thank You! Questions?
Aliaksei Antonau
5. Juli 2016