CONCEPT NOISE REDUCTION IN DEEP QUESTION ANSWERING SYSTEMS
First Claim
1. A method for a deep question answering system, the method comprising:
- computing a concept score for a first concept in a first case received by the deep question answering system, wherein the concept score is based on a machine learning concept model for the first concept;
excluding the first concept from consideration when analyzing a candidate answer and an item of supporting evidence to generate a response to the first case when the concept score does not exceed a predefined concept minimum weight threshold; and
increasing, by operation of one or more computer processors, a weight applied to the first concept when analyzing the candidate answer and the item of supporting evidence to generate the response to the first case when the concept score exceeds a predefined maximum weight threshold.
1 Assignment
0 Petitions
Accused Products
Abstract
Method, computer program product, and system to perform an operation for a deep question answering system. The operation begins by computing a concept score for a first concept in a first case received by the deep question answering system, the concept score being based on a machine learning concept model for the first concept. The operation then excludes the first concept from consideration when analyzing a candidate answer and an item of supporting evidence to generate a response to the first case upon determining that the concept score does not exceed a predefined concept minimum weight threshold. The operation then increases a weight applied to the first concept when analyzing the candidate answer and the item of supporting evidence to generate the response to the first case when the concept score exceeds a predefined maximum weight threshold.
-
Citations
8 Claims
-
1. A method for a deep question answering system, the method comprising:
-
computing a concept score for a first concept in a first case received by the deep question answering system, wherein the concept score is based on a machine learning concept model for the first concept; excluding the first concept from consideration when analyzing a candidate answer and an item of supporting evidence to generate a response to the first case when the concept score does not exceed a predefined concept minimum weight threshold; and increasing, by operation of one or more computer processors, a weight applied to the first concept when analyzing the candidate answer and the item of supporting evidence to generate the response to the first case when the concept score exceeds a predefined maximum weight threshold. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
Specification