Deep Learning of Knowledge Graph Embeddings for Semantic Parsing of Twitter Dialogs

被引：0

作者：

Heck, Larry ^{[1
]}

Huang, Hongzhao ^{[2
]}

机构：

[1] Microsoft Res, Redmond, WA 98052 USA

[2] Rensselaer Polytech Inst, Troy, NY 12181 USA

来源：

2014 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2014年

关键词：

deep learning; semantic parsing; Twitter; dialog;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a novel method to learn neural knowledge graph embeddings. The embeddings are used to compute semantic relatedness in a coherence-based semantic parser. The approach learns embeddings directly from structured knowledge representations. A deep neural network approach known as Deep Structured Semantic Modeling (DSSM) is used to scale the approach to learn neural embeddings for all of the concepts (pages) of Wikipedia. Experiments on Twitter dialogs show a 23.6% reduction in semantic parsing errors compared to the state-of-the-art unsupervised approach.

引用

页码：597 / 601

页数：5

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