Knowledge Represention for Context and Sentiment Analysis

被引：0

作者：

Fakinlede, Ireti ^{[1
]}

Kumar, Vive ^{[1
]}

Wen, Dunwei ^{[1
]}

机构：

[1] Athabasca Univ, Sch Comp & Informat Syst, Athabasca, AB, Canada

来源：

2013 IEEE 13TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2013) | 2013年

关键词：

knowledge representation; sentiment analysis; anthropomorphic agents; social context; semantic roles; natural language processing;

D O I：

10.1109/ICALT.2013.158

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents a knowledge representation framework for natural language understanding. Here we propose an automated knowledge acquisition mechanism that mirrors information extraction in human-human interaction. This framework utilizes knowledge based automatic role labeling and automatic concept learning together with a conceptual structure that captures intent and context. The resulting framework is to be used to improve the agent's ability to engage in social interaction with humans.

引用

页码：493 / 494

页数：2