Learning Better Embeddings for Rare Words Using Distributional Representations
Irina Sergienya, Hinrich Schütze · 2015
There are two main types of word representations: low-dimensional embeddings and high-dimensional distributional vectors, in which each dimension corresponds to a context word.In this paper, we initialize an embedding-learning model with distributional vectors.Evaluation on word similarity shows that this initialization significantly increases the quality of embeddings for rare words.