A Mixture Model for Learning Multi-Sense Word Embeddings
Table 3: Spearman’s rank correlation (ρ×100) on scws, using AvgSim and AvgSimC.
['Model', 'AvgSim', 'AvgSimC']
We evaluate our model mswe by using AvgSim and AvgSimC on the benchmark scws dataset which considers effects of the contextual information on the word similarity task. Although we produce better scores than \newciteneelakantan:2014 and \newcitechen-liu-sun:2014 when using GlobalSim, we are outperformed by them when using AvgSim and AvgSimC. \newciteneelakantan:2014 clustered the embeddings of the context words around each target word to predict its sense and \newcitechen-liu-sun:2014 used pre-trained word embeddings to initialize vector representations of senses taken from WordNet, while we use a fixed number of topics as senses for words in mswe.