Published Date: 2018-02-08
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.
Co-occurrences between two words provide useful insights into the semantics of those words.Consequently, numerous prior work on word embedding learning has used co-occurrences between two wordsas the training signal for learning word embeddings.However, in natural language texts it is common for multiple words to be related and co-occurring in the same context.We extend the notion of co-occurrences to cover k(≥2)-way co-occurrences among a set of k-words.Specifically, we prove a theoretical relationship between the joint probability of k(≥2) words, and the sum of l_2 norms of their embeddings. Next, we propose a learning objective motivated by our theoretical resultthat utilises k-way co-occurrences for learning word embeddings.Our experimental results show that the derived theoretical relationship does indeed hold empirically, anddespite data sparsity, for some smaller k(≤5) values, k-way embeddings perform comparably or better than 2-way embeddings in a range of tasks.