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

Understand

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 \emph{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.