Two deep parsing components, an English Slot Grammar (ESG) parser and a predicate-argument structure (PAS) builder, provide core linguistic analyses of both the questions and the text content used by IBM Watson to find and hypothesize answers. Specifically, these components are fundamental in question analysis, candidate generation, and analysis of passage evidence. As part of the Watson project, ESG was enhanced, and its performance on Jeopardy! questions and on established reference data was improved. PAS was built on top of ESG to support higher-level analytics. In this paper, we describe these components and illustrate how they are used in a pattern-based relation extraction component of Watson. We also provide quantitative results of evaluating the component-level performance of ESG parsing.
%0 Journal Article
%1 McCordMurdockEtAl12ibmjrd
%A McCord, Michael C.
%A Murdock, J. William
%A Boguraev, Branimir
%D 2012
%J IBM Journal of Research and Development
%K 01801 ieee paper ibm ai language processing analysis zzz.iui
%N 3/4
%P 3:1--3:15
%R 10.1147/JRD.2012.2185409
%T Deep Parsing in Watson
%V 56
%X Two deep parsing components, an English Slot Grammar (ESG) parser and a predicate-argument structure (PAS) builder, provide core linguistic analyses of both the questions and the text content used by IBM Watson to find and hypothesize answers. Specifically, these components are fundamental in question analysis, candidate generation, and analysis of passage evidence. As part of the Watson project, ESG was enhanced, and its performance on Jeopardy! questions and on established reference data was improved. PAS was built on top of ESG to support higher-level analytics. In this paper, we describe these components and illustrate how they are used in a pattern-based relation extraction component of Watson. We also provide quantitative results of evaluating the component-level performance of ESG parsing.
@article{McCordMurdockEtAl12ibmjrd,
abstract = {Two deep parsing components, an English Slot Grammar (ESG) parser and a predicate-argument structure (PAS) builder, provide core linguistic analyses of both the questions and the text content used by IBM Watson to find and hypothesize answers. Specifically, these components are fundamental in question analysis, candidate generation, and analysis of passage evidence. As part of the Watson project, ESG was enhanced, and its performance on Jeopardy! questions and on established reference data was improved. PAS was built on top of ESG to support higher-level analytics. In this paper, we describe these components and illustrate how they are used in a pattern-based relation extraction component of Watson. We also provide quantitative results of evaluating the component-level performance of ESG parsing.},
added-at = {2017-11-13T14:44:56.000+0100},
author = {McCord, Michael C. and Murdock, J. William and Boguraev, Branimir},
biburl = {https://www.bibsonomy.org/bibtex/24d5d330db8f2fcd6f5f3872be3dfdbe4/flint63},
doi = {10.1147/JRD.2012.2185409},
file = {IEEE Digital Library:2012/McCordMurdockEtAl12ibmjrd.pdf:PDF},
groups = {public},
interhash = {11fdd03f96615e054e8328023ae2e323},
intrahash = {4d5d330db8f2fcd6f5f3872be3dfdbe4},
issn = {0018-8646},
journal = {IBM Journal of Research and Development},
keywords = {01801 ieee paper ibm ai language processing analysis zzz.iui},
number = {3/4},
pages = {3:1--3:15},
timestamp = {2018-04-16T12:16:59.000+0200},
title = {Deep Parsing in {Watson}},
username = {flint63},
volume = 56,
year = 2012
}