mlsanityData to 7 Oct 2026

Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction

arXiv 1804.07849 ↗First posted 20 Apr 2018cs.CLcited by 17

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Abstract (arXiv)

We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent (SGD): a novel generalization of the classical Brown clustering objective and a recently proposed variational lower bound. While both objectives are subject to noise in gradient updates, we show through analysis and experiments that the variational lower bound is robust whereas the generalized Brown objective is vulnerable. We obtain competitive performance on a multitude of datasets and languages with a simple architecture that encodes morphology and context.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2018.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 2; 2024: 2; 2025: 2; 2026: 1.

17 papers.

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