Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences

ACL 2019

Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences

Mar 24, 2021
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Abstract: A standard word embedding algorithm, such as word2vec and glove, makes a strong assumption that words are likely to be semantically related only if they co-occur locally within a window of fixed size. However, this strong assumption may not capture the semantic association between words that co-occur frequently but non-locally within documents. In this paper, we propose a graph-based word embedding method, named ‘word-node2vec’. By relaxing the strong constraint of locality, our method is able to capture both the local and non-local co-occurrences. Word-node2vec constructs a graph where every node represents a word and an edge between two nodes represents a combination of both local (e.g. word2vec) and document-level co-occurrences. Our experiments show that word-node2vec outperforms word2vec and glove on a range of different tasks, such as predicting word-pair similarity, word analogy and concept categorization. Authors: Procheta Sen, Debasis Ganguly, Gareth Jones (Dublin City University, IBM Research)

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