Word Embeddings, Sense Embeddings and their Application to Word Sense Induction

This paper investigates the cutting-edge techniques for word embedding, sense embedding, and our evaluation results on large-scale datasets. Word embedding refers to a kind of methods that learn a distributed dense vector for each word in a vocabulary. Traditional word embedding methods first obtain the co-occurrence matrix then perform dimension reduction with PCA. Recent methods use neural language models that directly learn word vectors by predicting the context words of the target word. Moving one step forward, sense embedding learns a distributed vector for each sense of a word. They either define a sense as a cluster of contexts where the target word appears or define a sense based on a sense inventory. To evaluate the performance of the state-of-the-art sense embedding methods, I first compare them on the dominant word similarity datasets, then compare them on my experimental settings. In addition, I show that sense embedding is applicable to the task of word sense induction (WSI). Actually we are the first to show that sense embedding methods are competitive on WSI by building sense-embedding-based systems that demonstrate highly competitive performances on the SemEval 2010 WSI shared task. Finally, I propose several possible future research directions on word embedding and sense embedding. The University of Rochester Computer Science Department supported this work.