Right-truncatable Neural Word Embeddings
Jun Suzuki, Masaaki Nagata · 2016
This paper proposes an incremental learning strategy for neural word embedding methods, such as SkipGrams and Global Vectors.Since our method iteratively generates embedding vectors one dimension at a time, obtained vectors equip a unique property.Namely, any right-truncated vector matches the solution of the corresponding lower-dimensional embedding.Therefore, a single embedding vector can manage a wide range of dimensional requirements imposed by many different uses and applications.