Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts.

ABSTRACT: Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing similar surrounding words are semantically close. We argue that local contexts can only partially define word semantics in the unsupervised word embedding learning. Global contexts, referring to the broader semantic units, such as the document or paragraph where the word appears, can capture different aspects of word semantics and complement local contexts. We propose two simple yet effective unsupervised word embedding models that jointly model both local and global contexts to learn word representations. We provide theoretical interpretations o

SUBMITTER: Meng Y 

PROVIDER: S-EPMC7931948 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature