11 research outputs found
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Learning word embeddings has received a significant amount of attention
recently. Often, word embeddings are learned in an unsupervised manner from a
large collection of text. The genre of the text typically plays an important
role in the effectiveness of the resulting embeddings. How to effectively train
word embedding models using data from different domains remains a problem that
is underexplored. In this paper, we present a simple yet effective method for
learning word embeddings based on text from different domains. We demonstrate
the effectiveness of our approach through extensive experiments on various
down-stream NLP tasks.Comment: 7 pages, accepted by EMNLP 201