Papers with Brown clustering

2 papers
Accelerated High-Quality Mutual-Information Based Word Clustering (2020.lrec-1)

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Challenge: Word clustering is a hard hierarchical clustering that uses short-range distributional information to construct clusters.
Approach: They propose to use a hierarchical clustering algorithm with a fixed-width beam to build clusters that outperform other word representations.
Outcome: The proposed method outperforms the original methods in the computation of hierarchical and flat clusters.
Offensive Language Detection Using Brown Clustering (2020.lrec-1)

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Challenge: a recent study shows that Brown clustering is of little use when distinguishing word polarity in sentiment analysis tasks.
Approach: They investigate the use of Brown clustering for offensive language detection . they train Brown clusters separately on positive and negative sentiment data, then combine it into a single complex feature per word .
Outcome: The proposed method improves offensive language detection when used as the only feature or with words or character n-grams.

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