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.
%0 Journal Article
%1 LallyPragerEtAl12ibmjrd
%A Lally, Adam
%A Prager, John M.
%A McCord, Michael C.
%A Boguraev, Branimir
%A Patwardhan, Siddharth
%A Fan, James
%A Fodor, Paul
%A Chu-Carroll, Jennifer
%D 2012
%J IBM Journal of Research and Development
%K 01801 ieee paper ibm ai language processing analysis algorithm zzz.iui
%N 3/4
%P 2:1--2:14
%R 10.1147/JRD.2012.2184637
%T Question Analysis: How Watson Reads a Clue
%V 56
%X 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.
@article{LallyPragerEtAl12ibmjrd,
abstract = {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.},
added-at = {2017-11-13T14:44:55.000+0100},
author = {Lally, Adam and Prager, John M. and McCord, Michael C. and Boguraev, Branimir and Patwardhan, Siddharth and Fan, James and Fodor, Paul and Chu-Carroll, Jennifer},
biburl = {https://www.bibsonomy.org/bibtex/29909d60962c22b0af67594f8022afeea/flint63},
doi = {10.1147/JRD.2012.2184637},
file = {IEEE Digital Library:2012/LallyPragerEtAl12ibmjrd.pdf:PDF},
groups = {public},
interhash = {3b8707823e466a5a3f31947bebf33bd0},
intrahash = {9909d60962c22b0af67594f8022afeea},
issn = {0018-8646},
journal = {IBM Journal of Research and Development},
keywords = {01801 ieee paper ibm ai language processing analysis algorithm zzz.iui},
number = {3/4},
pages = {2:1--2:14},
timestamp = {2018-04-16T12:36:35.000+0200},
title = {Question Analysis: How {Watson} Reads a Clue},
username = {flint63},
volume = 56,
year = 2012
}