How is quality of the word vectors measured?
They evaluate it on the word analogy, word similarity and synonym selection tasks using Pearson correlation coefficient as the metric.
Word2Vec is the most popular model for word representation and has been widely investigated in literature. However, its noise distribution for negative sampling is decided by empirical trials and the optimality has always been ignored. We suggest that the distribution is a sub-optimal choice, and propose to use a sub-sampled unigram distribution for better negative sampling. Our contributions include: (1) proposing the concept of semantics quantification and deriving a suitable sub-sampling rate for the proposed distribution adaptive to different training corpora; (2) demonstrating the advantages of our approach in both negative sampling and noise contrastive estimation by extensive evaluation tasks; and (3) proposing a semantics weighted model for the MSR sentence completion task, resulting in considerable improvements. Our work not only improves the quality of word vectors but also benefits current understanding of Word2Vec.
The recent decade has witnessed the great success achieved by word representation in natural language processing (NLP). It proves to be an integral part of most other NLP tasks, in which words have to be vectorized before input to the models. High quality word vectors have boosted the performance of many tasks, such as named entity recognition BIBREF0, BIBREF1, sentence completion BIBREF2, BIBREF3, part-of-speech tagging BIBREF4, BIBREF5, sentiment analysis BIBREF6, BIBREF7, and machine translation BIBREF8, BIBREF9. In a conventional way, word vectors are obtained from word-context co-occurrence matrices by either cascading the row and column vectors BIBREF10 or applying singular value decomposition (SVD) BIBREF11. However, these approaches are limited by their sub-optimal linear structure of vector space and the highly increased memory requirement when confronting huge vocabularies. Both problems have been solved by a popular model called Word2Vec BIBREF12, which utilizes two shallow neural networks, i.e., skip-gram and continuous bag-of-words, to learn word vectors from large corpora. The model is also capable of capturing interesting linear relationships between word vectors. While Word2Vec makes a breakthrough in word representation, it has not been fully understood and its theoretical exploitation is still in demand. One aspect, which has always been ignored, is the choice of noise distribution for negative sampling. Word2Vec employs a smoothed unigram distribution with a power rate of 3/4 as the noise distribution. The decision is made by empirical trials but has been widely adopted in subsequent work BIBREF13, BIBREF4, BIBREF14, BIBREF15. However, the quality of learned word vectors is sensitive to the choice of noise distribution BIBREF16, BIBREF13 when using a moderate number (5 to 15) of negative samples, which is a common strategy for the tradeoff between vector quality and computation costs. In this paper, we propose to employ a sub-sampled unigram distribution for negative sampling and demonstrate its capability of improving the linear relationships between word vectors. Our contributions include three aspects: (1) We propose the concept of semantics quantification and derive a suitable sub-sampling rate for the proposed distribution. (2) We demonstrate the advantages of our noise distribution in both negative sampling and noise contrastive estimation by extensive experiments. (3) We propose a semantics weighted model for the MSR sentence completion task, resulting in considerable improvements.