Paper Info
Title
Word Representation With Salient Features.
Abstract
Inspired from the idea that the contexts in which a word occurs are of different significance, this paper proposes a novel method, called word representation with Salient Features (SaFe), to represent words using salient features selected from the context words. The SaFe method employs the point-wise mutual information (PMI) method with scaled context window to measure word association between a target word and its context. Then, contexts having word associations will be selected as salient features, where the number of salient features for a given word is decided by the ratio between the number of unique contexts and the total counts of occurrences in the whole corpus. The SaFe method can be used with the positive PMI matrix (PPMI), with each row representing a word, hence the name SaFe-PPMI. Moreover, the SaFe-PPMI model can be further decomposed by using the truncated singular vector decomposition technique to obtain dense vectors. In addition to efficient computation, the new models can achieve remarkable improvements in seven semantic relatedness tasks, and they show superior performance when compared with the state-of-the-art models.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2892817
IEEE ACCESS
Keywords
Field
DocType
Point-wise mutual information,salient features,singular vector decomposition,word representation
Semantic similarity,Task analysis,Computer science,Vector decomposition,Context model,Artificial intelligence,Natural language processing,Mutual information,Measure word,Semantics,Distributed computing,Salient
Journal
Volume
ISSN
Citations 
7
2169-3536
2
PageRank 
References 
Authors
0.38
0
4
Name
Order
Citations
PageRank
Ming Zhang18918.62
Vasile Palade21353114.44
Yan Wang35412.95
Zhicheng Ji4154.05