1. A Novel Approach to Event Duration Prediction
Pranav Khaitan
Divye Raj Khilnani
Ye Jin

2. Introduction Liverpool will be playing inter-Milan this Friday.
Predicting event duration has been a challenging problem and can solve some major challenges being faced in question answering systems.
Examples:
Liverpool will be playing inter-Milan this Friday.
The United States has been fighting a cold war with the Soviet Union.
duration of the match is in hours
duration of the war was in decades

3. Duration is Non-trivial
Same event can have different bounds in different contexts.
James watched a movie.
Hour
James watched the birds fly.
Minute
More Features
Subject
Object
Grammatical
Part of Speech
Tense
Modality
Context
Class
Hypernym
Aspect

4. System Design Feature Extraction Feature Selection Learning and
Classification
Evaluation
Parse Tree
Web Count
Hypernym
Named Entity Recognition
X2 score
MI score
Emperical observation
Supervised learning:
Naïve Bayes
Logistic Regression
Maximum Entropy
Unsupervised Learning
Agglomerative Clustering
Multinomial clustering
Precision
Recall F1
Kappa
Approximate Agreement

5. Feature Analysis Subject-object Base verb lemmatization Tense
Jonathan is watching a movie vs Jonathan is watching an advertisement
Base verb lemmatization
eating, ate, has eaten, will be eating
Tense
Jonathan will play football in the evening vs Jonathan has been playing football for the past ten years
Sentential Dependencies
He read the report quickly vs He read the report slowly
Part of speech tagging
The government’s move was anticipated
Named Entity Recognition
The body will define the role of the United Nations

6. Feature Analysis Hypernyms Contextual Features Web Counts
Generic Features: Modality, Aspect, Class
Report Feature

7. Results

8. Results

9. Feature Selection
Total extracted features: 10,000+. Need to scale down. 　
MI score for features drops quickly
Effectiveness of feature selection

10. Unsupervised Clustering

11. Conclusion
Significant gain in event duration prediction accuracy using supervised learning
Unsupervised learning results look promising and gives opportunity to do duration prediction across domains with little annotated data
Important to automatically select features and reduce human involvement
Classification Task
Our Results
Feng Pan et al
Human Agreement
Coarse Grain
75.16%
70.3%
87.7%
Fine Grain
63.69%
65.8%
79.8%