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A Novel Approach to Event Duration Prediction

A Novel Approach to Event Duration Prediction. Pranav Khaitan Divye Raj Khilnani Ye Jin. Introduction. Predicting event duration has been a challenging problem and can solve some major challenges being faced in question answering systems. Examples:.

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A Novel Approach to Event Duration Prediction

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

  2. Introduction 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 Minute • James watched the birds fly. More Features Subject Aspect Grammatical Hypernym Object Class Context Part of Speech Modality Tense

  4. System Design Evaluation Learning and Classification Feature Extraction Feature Selection • X2score • MI score • Empericalobservation • Supervised learning: • Naïve Bayes • Logistic Regression • Maximum Entropy • Unsupervised Learning • Agglomerative Clustering • Multinomial clustering • Parse Tree • Web Count • Hypernym • Named Entity Recognition • Precision • Recall • F1 • Kappa • Approximate Agreement

  5. Feature Analysis • Subject-object • 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 • Contextual Features • 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

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