%0 Journal Article %1 MurdockKalyanpurEtAl12ibmjrd %A Murdock, J. William %A Kalyanpur, Aditya %A Welty, Chris %A Fan, James %A Ferrucci, David A. %A Gondek, David %A Zhang, Lei %A Kanayama, Hiroshi %D 2012 %J IBM Journal of Research and Development %K 01801 ieee paper ibm ai language semantic processing answer algorithm zzz.iui %N 3/4 %P 7:1--7:13 %R 10.1147/JRD.2012.2187036 %T Typing Candidate Answers Using Type Coercion %V 56 %X Many questions explicitly indicate the type of answer required. One popular approach to answering those questions is to develop recognizers to identify instances of common answer types (e.g., countries, animals, and food) and consider only answers on those lists. Such a strategy is poorly suited to answering questions from the Jeopardy! television quiz show. Jeopardy! questions have an extremely broad range of types of answers, and the most frequently occurring types cover only a small fraction of all answers. We present an alternative approach to dealing with answer types. We generate candidate answers without regard to type, and for each candidate, we employ a variety of sources and strategies to judge whether the candidate has the desired type. These sources and strategies provide a set of type coercion scores for each candidate answer. We use these scores to give preference to answers with more evidence of having the right type. Our question-answering system is significantly more accurate with type coercion than it is without type coercion; these components have a combined impact of nearly 5 percent on the accuracy of the IBM Watson question-answering system.

@article{MurdockKalyanpurEtAl12ibmjrd, abstract = {Many questions explicitly indicate the type of answer required. One popular approach to answering those questions is to develop recognizers to identify instances of common answer types (e.g., countries, animals, and food) and consider only answers on those lists. Such a strategy is poorly suited to answering questions from the Jeopardy! television quiz show. Jeopardy! questions have an extremely broad range of types of answers, and the most frequently occurring types cover only a small fraction of all answers. We present an alternative approach to dealing with answer types. We generate candidate answers without regard to type, and for each candidate, we employ a variety of sources and strategies to judge whether the candidate has the desired type. These sources and strategies provide a set of type coercion scores for each candidate answer. We use these scores to give preference to answers with more evidence of having the right type. Our question-answering system is significantly more accurate with type coercion than it is without type coercion; these components have a combined impact of nearly 5 percent on the accuracy of the IBM Watson question-answering system.}, added-at = {2017-11-13T14:44:58.000+0100}, author = {Murdock, J. William and Kalyanpur, Aditya and Welty, Chris and Fan, James and Ferrucci, David A. and Gondek, David and Zhang, Lei and Kanayama, Hiroshi}, biburl = {https://www.bibsonomy.org/bibtex/27950636384b6ce6ef8a537ace0d05f62/flint63}, doi = {10.1147/JRD.2012.2187036}, file = {IEEE Digital Library:2012/MurdockKalyanpurEtAl12ibmjrd.pdf:PDF}, groups = {public}, interhash = {a7cd727c910fa1fa1f1d94de970ae329}, intrahash = {7950636384b6ce6ef8a537ace0d05f62}, issn = {0018-8646}, journal = {IBM Journal of Research and Development}, keywords = {01801 ieee paper ibm ai language semantic processing answer algorithm zzz.iui}, number = {3/4}, pages = {7:1--7:13}, timestamp = {2018-04-16T12:06:19.000+0200}, title = {Typing Candidate Answers Using Type Coercion}, username = {flint63}, volume = 56, year = 2012 }