Event Detection and Summarization in Weblogs with Temporal Collocations

1
Event Detection and Summarization in Weblogs with
Temporal Collocations

• Chun-Yuan Teng and Hsin-Hsi Chen
• Department of Computer Science and Information
  Engineering
• National Taiwan University
• Taipei, Taiwan
• hhchen_at_csie.ntu.edu.tw

2
Outlines

• Motivation
• Temporal collocation
• Event detection and summarization using temporal
  collocations
• Experiments
• Datasets
• Evaluation of event detection
• Evaluation of event summarization
• Conclusion

3
Motivation

• Weblogs
• containing abundant life experiences and public
  opinions toward different topics
• highly sensitive to the events occurring in the
  real world
• associated with the personal information of
  bloggers
• Problem
• How to know what bloggers write and discuss over
  time?
• Event detection is fundamental

4
Google Trend

• Google Trend
• Plot the frequency of word and frequency of news
  over time
• E.g., Select the news with highest frequency of
  president
• Ambiguous peak
• We dont know the peak of president is caused
  by which president.

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Collocations

• Combination of words give the specific meaning.
• Collocations such as mean and variance,
  hypothesis test, mutual information, etc. are
  used to model the relationship between terms.
• Can we model collocations over time?

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Temporal Collocation

• Mutual Information
• Temporal Mutual Information
• P(x,yt) denotes the probability of co-occurrence
  of terms x and y in timestamp t.
• P(xt) and P(yt) denote the probability of x and
  y in timestamp t.

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Temporal Collocation

• Change of Temporal Mutual Information
• C(x,y,t1,t2) is the change of temporal mutual
  information of terms x and y in time interval
  t1, t2
• I(x,y t1) and I(x,y t2) are the temporal mutual
  information in time stamps t1 and t2, respectively

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Event Detection

• Identify the collocations resulting in events
• Retrieve the descriptions of events

9
System Architecture

• Pre-processing phase
• parse the weblogs
• retrieve the collocations
• Event detection phase
• detect the unusual peak of the change of temporal
  mutual information
• identify the set of collocations resulting in an
  event in a specific time duration
• Event summarization phase
• extract the collocations related to the seed
  collocations found in a specific time duration

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Pre-processing Phase

• Retrieve the collocations from the sentences in
  blog posts
• Propose the candidates within a window size
• Remove those candidates containing stop-words or
  with low change of temporal mutual information

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Event Detection Phase

• Remove the regular pattern by seasonal index
• Measure the unusual peak of temporal mutual
  information to detect the plausible events
• change of temporal mutual information
• (MI2-MI1)
• favor the events with high frequency
• relative change of temporal mutual information
• (MI2-MI1)/MI1
• favor the events with low mutual information

MI1 and MI2 temporal mutual information at
timestamps t1 and t2
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Event Summarization Phase

• Select the collocations with the highest mutual
  information with the word w in a seed collocation
• Place the seed collocation into a collocation
  network
• Add the collocation having the highest mutual
  information
• Compute the mutual information of the multiword
  collocations when a new collocation is added
• Stop and return the words in the collocation
  network if the multiword mutual information is
  lower than a threshold

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A Collocation Network
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Data Sets

• ICWSM weblog data set
• collected from May 1, 2006 through May 20, 2006
• about 20 GB
• the English weblog of 2,734,518 articles for
  analysis
• Gold standard
• http//en.wikipedia.org/wiki/May_2006
• The events posted in wikipedia are not always
  complete, thus we adopt recall rate
• The events specified in wikipedia are not always
  discussed in weblogs, thus we remove the events
  listed in wikipedia, but not referenced in the
  weblogs

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Evaluation of Event Detection Phase
recall rate 75
16
Performance of Event Detection Phase
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Discussion

• Change of MI (left) favors regular events and
  events with high frequency
• Time May 03
• Feeling fell left
• Relative change (right) favors person or special
  event
• Terrorists killed in May 3 zacarias moussaoui,
  parad mahajan
• best actress award in golden globe award in May
  3 Geena Davis

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Evaluation of Event Summarization

• Method 1 Employ the highest temporal mutual
  information
• Method 2 Utilize the highest product of temporal
  mutual information and change of temporal mutual
  information

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An Example of Event Retrieval

• typhoon Chanchu
• The typhoon Chanchu appears in the pacific ocean
  near 5/10, and the typhoon passes through
  Philippine and China and result in disasters in
  these areas.

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Event Summarization for Typhoon Chanchu Using
Method 1
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Event Summarization for Typhoon Chanchu Using
Method 2
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Some Observations

• The appearance of the typhoon Chanchu cannot be
  found from the events listed in wikipedia on May
  10.
• We can identify the appearance of typhoon Chanchu
  from the description of the typhoon appearance
  such as typhoon named and Typhoon eye.
• The typhoon Chanchus path can also be inferred
  from the retrieved collocations such as
  Philippine China and near China.
• The responses of bloggers such as unexpected
  typhoon and 8 typhoons are also extracted.

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Method 1 vs. Method 2

• Method 1 shows more noise than Method 2.
• The term typhoon earthquake is extracted using
  the Method 1.
• The term typhoon earthquake is not retrieved
  using Method 2 because we also consider the
  change of temporal mutual information.

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Concluding Remarks

• The works we have done
• Introduce temporal mutual information to capture
  term-term association over time in weblogs
• Select the extracted collocation with unusual
  peak in terms of relative change of temporal
  mutual information to represent an event
• Collect those collocations with the highest
  product of mutual information and change of
  temporal mutual information to summarize the
  specific event
• Future works
• Model the collocations over time and location
• Model the relationship between the user-preferred
  usage of collocations and the profile of users

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Thanks