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        <title>Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences</title>
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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.</abstract>
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%0 Conference Proceedings
%T Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences
%A Sen, Procheta
%A Ganguly, Debasis
%A Jones, Gareth
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F sen-etal-2019-word
%X 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.
%R 10.18653/v1/N19-1109
%U https://aclanthology.org/N19-1109
%U https://doi.org/10.18653/v1/N19-1109
%P 1041-1051

Markdown (Informal)

[Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences](https://aclanthology.org/N19-1109) (Sen et al., NAACL 2019)

• Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences (Sen et al., NAACL 2019)

ACL

• Procheta Sen, Debasis Ganguly, and Gareth Jones. 2019. Word-Node2Vec: Improving Word Embedding with Document-Level Non-Local Word Co-occurrences. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1041–1051, Minneapolis, Minnesota. Association for Computational Linguistics.