Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words

Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Main track. Pages 3970-3976.

Abstract

Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks.

However, their performances vary significantly across different tasks,

implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics.

Therefore, we believe that it is important to combine the existing word embeddings to produce more accurate and complete

meta-embeddings of words.

For this purpose, we propose an unsupervised locally linear meta-embedding learning method that takes

pre-trained word embeddings as the input, and produces more accurate meta embeddings.

Unlike previously proposed meta-embedding learning methods that learn a global projection over all words in a vocabulary,

our proposed method is sensitive to the differences in local neighbourhoods of the individual source word embeddings.

Moreover, we show that vector concatenation, a previously proposed highly competitive baseline

approach for integrating word embeddings, can be derived as a special case of the proposed method.

Experimental results on semantic similarity, word analogy, relation classification, and short-text classification tasks show that our

meta-embeddings to significantly outperform prior methods in several benchmark datasets,

establishing a new state of the art for meta-embeddings.