On Learning Word Embeddings From Linguistically Augmented Text Corpora
Amila Silva, Chathurika Amarathunga · 2019
Word embedding learning is a technique in Natural Language Processing (NLP) to map words into vector space representations, is one of the most popular research directions in modern NLP by virtue of its potential to boost the performance of many NLP downstream tasks.Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality representations if the text corpus is small and sparse.This paper proposes a method to generate effective word embeddings from limited data.Empirically, we show that the proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.