Using k-Way Co-Occurrences for Learning Word Embeddings

Danushka Bollegala, Yuichi Yoshida, Ken-ichi Kawarabayashi

Abstract: Co-occurrences between two words provide useful insights into the semantics of those words. Consequently, numerous prior work on word embedding learning have used co-occurrences between two words as 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(\geq\!\!2)$-way co-occurrences among a set of k$-words. Specifically, we prove a theoretical relationship between the joint probability of k(\geq\!\!2)$ words, and the sum of \ell_2$ norms of their embeddings. Next, we propose a learning objective motivated by our theoretical result that utilises k$-way co-occurrences for learning word embeddings. Our experimental results show that the derived theoretical relationship does indeed hold empirically, and despite data sparsity, for some smaller k$ values, k$-way embeddings perform comparably or better than 2$-way embeddings in a range of tasks.