Linear Ensembles of Word Embedding Models
This work addresses improving word embeddings for morphologically complex languages with small corpora, but it is incremental as it applies known linear combination techniques to a specific domain.
The paper tackled the problem of combining multiple word embedding models into an ensemble using linear methods, specifically comparing ordinary least squares regression and orthogonal Procrustes, and found that orthogonal Procrustes achieved 7-10% relative improvements in synonym tests and 19-47% in analogy tests over the mean of initial models on Estonian data.
This paper explores linear methods for combining several word embedding models into an ensemble. We construct the combined models using an iterative method based on either ordinary least squares regression or the solution to the orthogonal Procrustes problem. We evaluate the proposed approaches on Estonian---a morphologically complex language, for which the available corpora for training word embeddings are relatively small. We compare both combined models with each other and with the input word embedding models using synonym and analogy tests. The results show that while using the ordinary least squares regression performs poorly in our experiments, using orthogonal Procrustes to combine several word embedding models into an ensemble model leads to 7-10% relative improvements over the mean result of the initial models in synonym tests and 19-47% in analogy tests.