Carey Priebe


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An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces
Kelly Marchisio | Youngser Park | Ali Saad-Eldin | Anton Alyakin | Kevin Duh | Carey Priebe | Philipp Koehn
Findings of the Association for Computational Linguistics: EMNLP 2021

Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may be understood as nodes in a weighted graph. This framing allows us to examine a node’s graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at


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Cross-Instance Tuning of Unsupervised Document Clustering Algorithms
Damianos Karakos | Jason Eisner | Sanjeev Khudanpur | Carey Priebe
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference