D. Roth, C. Cumby, Xiao Li, P. Morie, R. Nagarajan, N. Rizzolo, K. Small, and W. Yih
We describe a machine learning centered approach to developing an open domain question answering system. The system was developed in the summer of 2002, building upon several existing machine learning based NLP modules developed within a unified framework.
Both queries and data were preprocessed and augmented with POS tagging, shallow parsing information, and some level of semantic categorization (beyond named entities) using a SNoW based machine learning approach. Given these as input, the system proceeds as an incremental constraint satisfaction process. A machine learning based question analysis module extracts structural and semantic constraints on the answer, including an NE classification of the desired answer type. The system continues in several steps to identify candidate passages and then extracts an answer that best satisfies the constraints.
With the available machine learning technologies, the system was developed in six weeks with the goal of identifying some of the key research issues of QA and challenges to it.
In Proceedings of TREC 2002
Publisher National Institute of Standards and Technology
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