Comparing word2vec and GloVe for Automatic Measurement of MWE Compositionality
Thomas Pickard · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2020
This paper explores the use of word2vec and GloVe embeddings for unsupervised measurement of the semantic compositionality of MWE candidates. Through comparison with several human-annotated reference sets, we find word2vec to be substantively superior to GloVe for this task. We also find Simple English Wikipedia to be a poor-quality resource for compositionality assessment, but demonstrate that a sample of 10% of sentences in the English Wikipedia can provide a conveniently tractable corpus with only moderate reduction in the quality of outputs.