The art of Prolog (2nd ed.): advanced programming techniques
The art of Prolog (2nd ed.): advanced programming techniques
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Natural Language Processing for PROLOG Programmers
Natural Language Processing for PROLOG Programmers
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
COGEX: a logic prover for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Open-domain question: answering
Foundations and Trends in Information Retrieval
Overview of the CLEF 2007 Multilingual Question Answering Track
Advances in Multilingual and Multimodal Information Retrieval
Analysis of strategic knowledge in back of the envelope reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning foci for question answering over topic maps
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Relation extraction and scoring in DeepQA
IBM Journal of Research and Development
Structured data and inference in DeepQA
IBM Journal of Research and Development
Special questions and techniques
IBM Journal of Research and Development
Fact-based question decomposition in DeepQA
IBM Journal of Research and Development
A framework for merging and ranking of answers in DeepQA
IBM Journal of Research and Development
Labeling by landscaping: classifying tokens in context by pruning and decorating trees
Proceedings of the 21st ACM international conference on Information and knowledge management
A comparison of hard filters and soft evidence for answer typing in watson
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
Introduction to "This is Watson"
IBM Journal of Research and Development
IBM Journal of Research and Development
Automatic knowledge extraction from documents
IBM Journal of Research and Development
Finding needles in the haystack: search and candidate generation
IBM Journal of Research and Development
Typing candidate answers using type coercion
IBM Journal of Research and Development
Textual evidence gathering and analysis
IBM Journal of Research and Development
Relation extraction and scoring in DeepQA
IBM Journal of Research and Development
Structured data and inference in DeepQA
IBM Journal of Research and Development
Special questions and techniques
IBM Journal of Research and Development
Identifying implicit relationships
IBM Journal of Research and Development
Fact-based question decomposition in DeepQA
IBM Journal of Research and Development
A framework for merging and ranking of answers in DeepQA
IBM Journal of Research and Development
In the game: the interface between Watson and Jeopardy!
IBM Journal of Research and Development
Joint question clustering and relevance prediction for open domain non-factoid question answering
Proceedings of the 23rd international conference on World wide web
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The first stage of processing in the IBM Watson™ system is to perform a detailed analysis of the question in order to determine what it is asking for and how best to approach answering it. Question analysis uses Watson's parsing and semantic analysis capabilities: a deep Slot Grammar parser, a named entity recognizer, a co-reference resolution component, and a relation extraction component. We apply numerous detection rules and classifiers using features from this analysis to detect critical elements of the question, including: 1) the part of the question that is a reference to the answer (the focus); 2) terms in the question that indicate what type of entity is being asked for (lexical answer types); 3) a classification of the question into one or more of several broad types; and 4) elements of the question that play particular roles that may require special handling, for example, nested subquestions that must be separately answered. We describe how these elements are detected and evaluate the impact of accurate detection on our end-to-end question-answering system accuracy.