Using $k$-way Co-occurrences for Learning Word Embeddings
This work addresses a limitation in natural language processing by incorporating multi-word relationships, though it is incremental as it builds on existing co-occurrence methods.
The authors tackled the problem of learning word embeddings by extending co-occurrence modeling from pairwise to k-way relationships, proving a theoretical link between joint probabilities and embedding norms and showing that k-way embeddings can perform comparably or better than pairwise embeddings in some tasks.
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.