An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This work presents a general rank-learning framework for passage ranking within Question Answering (QA) systems using linguistic and semantic features. The framework enables query-time checking of complex linguistic and semantic constraints over keywords. Constraints are composed of a mixture of keyword and named entity features, as well as features derived from semantic role labeling. The framework supports the checking of constraints of arbitrary length relating any number of keywords. We show that a trained ranking model using this rich feature set achieves greater than a 20% improvement in Mean Average Precision over baseline keyword retrieval models. We also show that constraints based on semantic role labeling features are particularly effective for passage retrieval; when they can be leveraged, an 40% improvement in MAP over the baseline can be realized.