1. A probabilistic model for retrospective news event detection
Zhiwei Li, Bin Wang, Mingjing Li and Wei-Ying Ma. A probabilistic model for retrospective news event detection. In the 28th Annual International ACM SIGIR Conference (SIGIR'2005), 2005.
Presenter: Suhan Yu

2. Introduction RED News event definition
Retrospective news event detection (RED) is defined as the discovery of previously unidentified event in historical news corpus.
News event definition
a specific thing happens at a specific place and time.
Consecutively reported by many news articles in a period.

3. Introduction Observation: This paper contribution include:
A news article contains two kinds of information:
Contents (most previous research work focus)
Timestamps (often ignored)
This paper contribution include:
Proposing a multi-modal RED algorithm (use content and time info)
Proposing an approach to determine the approximate number of events from the articles count-time distribution.

4. Characteristics of news articles and events
Halloween topics contains many events
Each year’s Halloween is an event.
The figure indicates the
two most important
characteristics
Events are peaks, but in
some situations, several
events could be overlapped
on time.
The start and end time of
reports to events on
different website are very
similar.
event

5. Multi-modal retrospective news event detection method
Representation of news articles and news events
News articles represented by four kinds of information:
Who (person)
Where (location)
What (keywords)
When (time) --define as the period between the first article and the last article (Time consists two values)
Define news article and event as:
The four kinds of information of a news article are independent:

6. The generative model of news articles
Contents
Unigram models to model contents
Model persons, locations and keywords by three models.
Timestamps
Gaussian Mixture Model (GMM) is chosen to model timestamps.
A peak is usually modeled by a Gaussian function, where the mean is the position of the peak and the variance is the duration of event.

7. The generative model of news articles
N=term space size

8. Learning model parameters
The model parameter can be estimated by Maximum Likelihood method.
X represents the corpus of news articles.
M and k are number of news articles and number of events.
Given an event j, the four kinds of information of the i-th article are conditional independent:
EM algorithm is generally applied to maximize log-likelihood.

9. Maximize log-likelihood
E-step
M-step (update parameters)
Word n. Like person=Mary
Vocabulary size

10. Maximize log-likelihood
M-step
Parameters of the GMM
mean
variances

11. How many events?
We assume only the salient peaks are corresponding to events.
Initial estimate of events number can be set as the number of peaks
Use hill-climbing approach to detect all peaks
Compute salient score for each of them
The top 20% peaks are defined as salient peaks.
Spitting/merging initial
peaks
to detect salient peaks,
we define salient scores
for peaks as:

12. Splitting/merging initial salient peaks
MDL (Minimum Description Length)
penalty
Np=person vocabulary size

13. Event summarization Maximum a Posterior (MAP)
is the label of news article

14. Algorithm summary

15. Multi-modal RED algorithm application
HISCOVERY system
HISCOVERY (HIStory disCOVERY)
Two useful function
Photo Story
Chronicle
News article come from 12 news sites (such as CNN, MSNBC, BBC…)

16. HISCOVERY system

17. Experimental methods Data Each year’s reports can be
TDT
Benchmarks for event detection.
TDT4
Run experiments
Contain 80 events annotated from news articles.
These articles collected from the period of 2000/10~2001/1
Each year’s reports can be
regarded as an events.
Extracting named entities.
Extracted by BBN NLP tool,
which can extract seven
types of named entities.

18. Experimental design To compare the approach with other algorithm:
Group Average Clustering (GAC)
It is the best algorithm in TDT evaluations.
A hierarchical clustering method
Baseline
kNN algorithm

19. Results Probabilistic model gains the best results, but the
improvement are
not significant.

20. Results
Named entities

21. result

22. result

23. result
39 events

24. result
46 events

25. Conclusion Study 2 characteristics of news articles and events.
Proposed a multi-modal RED algorithm
Future work:
Use fitful dynamic models to model news events.
HMM
ICA (Independent components analysis)