公开(公告)号：US20180075013A1

公开(公告)日：2018-03-15

申请号：US15473424

申请日：2017-03-29

Applicant: Infosys Limited

Inventor： Abdul Razack ,  Sudipto Dasgupta ,  Mayoor Rao ,  John Kuriakose

IPC: G06F17/27 ,  G06N99/00 ,  G06N5/02 ,  G06F17/30

Abstract: A method and system automates training named entity recognition in natural language processing to build configurable entity definitions includes receiving input documents or entities through an administration module and defining a domain for each entity. Further, one or more entities corresponding to the domain specific entity in the received documents are determined and a training file to one of pick a right parser, extract content and label the entity ambiguity is generated. One or more user actions are collected and maintained at a repository through a knowledge engine. Still further, one or more labelled ambiguous words are predicted and the knowledge engine is updated. Data may be fetched, through a training pipeline execution engine and each entity may be associated with one or more documents based on the fetched data from the document store to build configurable entity definitions.

公开(公告)号：US10558754B2

公开(公告)日：2020-02-11

申请号：US15473424

申请日：2017-03-29

Applicant: Infosys Limited

Inventor： Abdul Razack ,  Sudipto Dasgupta ,  Mayoor Rao ,  John Kuriakose

IPC: G06F17/27 ,  G06N5/02 ,  G06N20/00

Abstract: A method and system automates training named entity recognition in natural language processing to build configurable entity definitions includes receiving input documents or entities through an administration module and defining a domain for each entity. Further, one or more entities corresponding to the domain specific entity in the received documents are determined and a training file to one of pick a right parser, extract content and label the entity ambiguity is generated. One or more user actions are collected and maintained at a repository through a knowledge engine. Still further, one or more labelled ambiguous words are predicted and the knowledge engine is updated. Data may be fetched, through a training pipeline execution engine and each entity may be associated with one or more documents based on the fetched data from the document store to build configurable entity definitions.