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

Procheta Sen, Debasis Ganguly, Gareth Jones


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.
Anthology ID:
N19-1109
Volume:
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)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1041–1051
Language:
URL:
https://www.aclweb.org/anthology/N19-1109
DOI:
10.18653/v1/N19-1109
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1109.pdf
Video:
 https://vimeo.com/355773895