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Quantifying the morphosyntactic content of Brown Clusters

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Brown and Exchange word clusters have long been successfully used as word representations in Natural Language Processing (NLP) systems. Their success has been attributed to their seeming ability to represent both semantic and syntactic information. Using corpora representing several language families, we test the hypothesis that Brown and Exchange word clusters are highly effective at encoding morphosyntactic information. Our experiments show that word clusters are highly capable of distinguishing Parts of Speech. We show that increases in Average Mutual Information, the clustering algorithms' optimization goal, are highly correlated with improvements in encoding of morphosyntactic information. Our results provide empirical evidence that downstream NLP systems addressing tasks dependent on morphosyntactic information can benefit from word cluster features.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics
Volume1
PublisherAssociation for Computational Linguistics
Publication dateJun 2019
Pages1541–1550
Article numberN19-1157
ISBN (Print)978-1-950737-13-0
Publication statusPublished - Jun 2019

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