ML Anthology

Detecting Lexical Semantic Change Across Corpora with Smooth Manifolds (Student Abstract)

Anmol Goel, Ponnurangam Kumaraguru
AAAI 2021 pp. 15783-15784
doi:10.1609/AAAI.V35I18.17888 /aaai/2021/goel2021aaai-detecting/

Abstract

Comparing two bodies of text and detecting words with significant lexical semantic shift between them is an important part of digital humanities. Traditional approaches have relied on aligning the different embeddings using the Orthogonal Procrustes problem in the Euclidean space. This study presents a geometric framework that leverages smooth Riemannian manifolds for corpus-specific orthogonal rotations and a corpus-independent scaling metric to project the different vector spaces into a shared latent space. This enables us to capture any affine relationship between the embedding spaces while utilising the rich geometry of smooth manifolds.

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Cite

Text

Goel and Kumaraguru. "Detecting Lexical Semantic Change Across Corpora with Smooth Manifolds (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17888

Markdown

[Goel and Kumaraguru. "Detecting Lexical Semantic Change Across Corpora with Smooth Manifolds (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/goel2021aaai-detecting/) doi:10.1609/AAAI.V35I18.17888

BibTeX

@inproceedings{goel2021aaai-detecting,
  title     = {{Detecting Lexical Semantic Change Across Corpora with Smooth Manifolds (Student Abstract)}},
  author    = {Goel, Anmol and Kumaraguru, Ponnurangam},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15783-15784},
  doi       = {10.1609/AAAI.V35I18.17888},
  url       = {https://mlanthology.org/aaai/2021/goel2021aaai-detecting/}
}