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A Novel Approach to Event Duration Prediction

This study proposes a novel approach to predicting event duration, addressing major challenges in question answering systems. The approach incorporates various features and machine learning techniques for accurate prediction across domains with minimal human involvement.

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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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