Incorporating word order explicitly in GloVe word embedding
Word embedding is the process of representing words from a corpus of text as real number vectors. These vectors are often derived from frequency statistics from the source corpus. In the GloVe model as proposed by Pennington et al. these vectors are generated using a word word cooccurrence matrix. However, the GloVe model fails to explicitly take into account the order in which words appear within the contexts of other words. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. The most successful method involves directly concatenating several word vector matrices for each position in the context window. An improvement of 9.7% accuracy is achieved by using this explicit representation of word order with GloVe word embeddings.