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Word embedding has become ubiquitous and is widely used in various naturallanguage processing (NLP) tasks, such as web retrieval, web semantic analysis,and machine translation, and so on. Unfortunately, training the word embeddingin a relatively large corpus is prohibitively expensive. We propose agraph-based word embedding algorithm, called Word-Graph2vec, which converts thelarge corpus into a word co-occurrence graph, then takes the word sequencesamples from this graph by randomly traveling and trains the word embedding onthis sampling corpus in the end. We posit that because of the limitedvocabulary, huge idioms, and fixed expressions in English, the size and densityof the word co-occurrence graph change slightly with the increase in thetraining corpus. So that Word-Graph2vec has stable runtime on the large-scaledata set, and its performance advantage becomes more and more obvious with thegrowth of the training corpus. Extensive experiments conducted on real-worlddatasets show that the proposed algorithm outperforms traditional Word2vec fourto five times in terms of efficiency and two to three times than FastText,while the error generated by the random walk technique is small.

Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk technique