Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering
Jianbo Ye, Yanran Li, Zhaohui Wu, James Z. Wang, Wenjie Li, Jia Li · 2017
Word embeddings have become widelyused in document analysis.While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words.In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions.By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way.The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets.More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis.Experimental results with multiple embedding models are reported.