Comparison Das et al WSDM 2011 and Zhao et al AAAI 2007

From Cohen Courses
Revision as of 22:23, 5 November 2012 by Ysim (talk | contribs)
Jump to navigationJump to search

This is a comparison of two related papers in event detection and temporal information extraction.


The papers are

Comparative analysis of both papers

On a high level, both papers are interested in discovering events from large amount temporal information sources. Both of them leverage on user generated content, with Das et al using Wikipedia as their dataset, while Zhao et al used the Enron email corpus and Dailykos blogs.

In Das et al, their task was to first discover pairs of entities that were co-bursting in the same time period (of a week). Co-bursting means that both entities are mentioned significantly more than during other time periods. After which, the next step is to discover the relationships between such entities. This forms the foundation for an event, an n-ary relationship between entities that are bursty at the same time period.

Likewise, Zhao et al's task is to discover events, exploiting the temporal burstiness property of entities and text, and also the ``social aspect, where an event is being talked about more than usual by ``social actors.


They used the Enron email corpus and Dailykos blogs [3]. 30 events are manually labeled as ground truth in the dataset by looking for correspondance with real world news.

Performance is measured using precision/recall/fscore of how well events are recovered with their model.


They found that taking temporal and social dimensions into account can increase their f-score significantly. Their approach of integrating these diverse features together in a step-wise manner was also found to perform better than just including features in a standard machine learning framework.

Related papers

There has been a lot of work on event detection.

Study plan

  • Article: Adaptive time series model [4]
  • Graph cut based clustering [5]