Difference between revisions of "Comparison Das et al WSDM 2011 and Zhao et al AAAI 2007"

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The papers are
 
The papers are
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* Anish Das Sarma, A. Jain, C. Yu. [[Das_Sarma_et._al.,_Dynamic_Relationship_and_Event_Discovery,_WSDM_2011|Dynamic relationship and event discovery]]. In Proceedings of the fourth ACM international conference on Web search and data mining, 2011. [http://web.eecs.umich.edu/~congy/work/wsdm11.pdf]
 
* Qiankun Zhao, Prasenjit Mitra, and Bi Chen. [[Zhao_et_al,_AAAI_07|Temporal and information flow based event detection from social text streams]]. In Proceedings of the 22nd national conference on Artificial intelligence - Volume 2, pages 1501–1506. AAAI Press, 2007. [http://www.purdue.edu/discoverypark/vaccine/assets/pdfs/publications/pdf/Temporal%20and%20Information%20Flow%20Based.pdf]
 
* Qiankun Zhao, Prasenjit Mitra, and Bi Chen. [[Zhao_et_al,_AAAI_07|Temporal and information flow based event detection from social text streams]]. In Proceedings of the 22nd national conference on Artificial intelligence - Volume 2, pages 1501–1506. AAAI Press, 2007. [http://www.purdue.edu/discoverypark/vaccine/assets/pdfs/publications/pdf/Temporal%20and%20Information%20Flow%20Based.pdf]
* A. Das Sarma, A. Jain, C. Yu. [[Das_Sarma_et._al.,_Dynamic_Relationship_and_Event_Discovery,_WSDM_2011|Dynamic relationship and event discovery]]. In Proceedings of the fourth ACM international conference on Web search and data mining, 2011. [http://web.eecs.umich.edu/~congy/work/wsdm11.pdf]
 
  
 
== Comparative analysis of both papers ==
 
== Comparative analysis of both papers ==
  
The authors presents a method for detecting events from social text stream by exploiting more than just the textual content, but also exploring the temporal and social dimensions of their data.
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On a high level, both papers are interested in discovering events from large amount temporal information sources.
Social text streams are represented as multigraphs where each node denote an "actor" and an edge represents the information flow between two actors.
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Both of them leverage on user generated content, with Anish et al using Wikipedia as their dataset, while Zhao et al used the [[UsesDataset::Enron email corpus]] and [[UsesDataset::Dailykos political blogs]].
First, the authors did content based [[UsesMethod::clustering]] using a vector space model (tf-idf weights, cosine similarity, the works) and graph cut based clustering algorithm.
 
This clustering segments their data into topics.
 
 
 
For a given topic, they measure the "intensities" over time using a sliding time window and segment them into intervals using an adaptive time series model.
 
With the temporal segmentation, each topic is represented as a sequence of social network graphs over time.
 
The weight of edges between different actors in this graph denote their communication intensity, and one can measure the "information flow" between actors for a given topic over time.
 
 
 
With the above content, temporal and information flow data, they extract events by extracting text segments subject to constraints on these information. For instance, an event should be from the same time interval, be about the same topics and mainly between a certain sub group of social actors.
 
  
 
== Evaluation ==
 
== Evaluation ==

Revision as of 22:18, 5 November 2012

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

Papers

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 Anish et al using Wikipedia as their dataset, while Zhao et al used the Enron email corpus and Dailykos political blogs.

Evaluation

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.

Discussion

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]