Analysis of The Characteristics of Similar Words Computed by Word Embeddings

被引：0

作者：

Zhou, Shuhui ^{[1
]}

Liu, Peihan ^{[2
]}

Liu, Lizhen ^{[1
]}

Song, Wei ^{[1
]}

Cheng, Miaomiao ^{[1
]}

机构：

[1] Capital Normal Univ, Informat & Engn Coll, Beijing 100048, Peoples R China

[2] Shanghai Ocean Univ, AIEN Coll, Shanghai 201306, Peoples R China

来源：

PROCEEDINGS OF 2020 IEEE 10TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2020) | 2020年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

word similarity; word2vec; classification;

D O I：

10.1109/iceiec49280.2020.9152307

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Word2vec is a popular word embedding technique and has also gained a lot of attention in the NLP field. But word embedding based on distributed representation is deficient in the semantics of distribution. This defect often occurs when we use word similarity to find similar words of a seed word. This article analyzes these similar words based on this deficiency. We propose a novel classification criterion to effectively classify similar words into 7 categories. Finally, we listed the future research directions, hoping to solve the problem of word confusion effectively.

引用

页码：327 / 330

页数：4

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