Journal
ACM/SIGIR PROCEEDINGS 2018
Volume -, Issue -, Pages 825-834Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3209978.3210040
Keywords
Word Embeddings; Semantic Shifts; Bayesian Surprise
Categories
Funding
- National Science Foundation [1054199, 1633370, 1131500, 1620319]
- NSFC [61625107]
- Key program of Zhejiang Province [2015C01027]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1054199] Funding Source: National Science Foundation
- Direct For Social, Behav & Economic Scie [1131500] Funding Source: National Science Foundation
- Direct For Social, Behav & Economic Scie
- SBE Off Of Multidisciplinary Activities [1620319] Funding Source: National Science Foundation
- Divn Of Social and Economic Sciences [1131500] Funding Source: National Science Foundation
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Much work has been done recently on learning word embeddings from large corpora, which attempts to find the coordinates of words in a static and high dimensional semantic space. In reality, such corpora often span a sufficiently long time period, during which the meanings of many words may have changed. The co-evolution of word meanings may also result in a distortion of the semantic space, making these static embeddings unable to accurately represent the dynamics of semantics. In this paper, we present a novel computational method to capture such changes and to model the evolution of word semantics. Distinct from existing approaches that learn word embeddings independently from time periods and then align them, our method explicitly establishes the stable topological structure of word semantics and identifies the surprising changes in the semantic space over time through a principled statistical method. Empirical experiments on large-scale real-world corpora demonstrate the effectiveness of the proposed approach, which outperforms the state-of-the-art by a large margin.
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