Paper Info
Title
Semantic vector learning for natural language understanding.
Abstract
•We introduce semantic frame learning framework for natural language understanding.•We introduce neural architecture for learning vector representation of semantic frame.•Considering distance and task loss together shows best representation learning performance.•Vector semantic representation invoke many useful applications around NLU including sentence search, visualization, and re-ranking.
Year
DOI
Venue
2019
10.1016/j.csl.2018.12.008
Computer Speech & Language
Keywords
Field
DocType
Natural language understanding,Semantic frame learning,Deep learning,Distributed representation,Semantic vector,Semantic Corpus Visualization
Semantic memory,Vector space,Embedding,Semantic search,Computer science,Visualization,Natural language understanding,Artificial intelligence,Natural language processing,Word2vec,Machine learning
Journal
Volume
ISSN
Citations 
56
0885-2308
1
PageRank 
References 
Authors
0.37
17
1
Name
Order
Citations
PageRank
Sangkeun Jung119715.23