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 at 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 2019 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies
EditorsJill Burstein, Christy Doran, Thamar Solorio
Number of pages10
Volume1
Place of publicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Publication year2019
Pages1541-1550
ISBN (print)978-1-950737-13-0
Publication statusPublished - 2019
EventAnnual Conference of the North American Chapter of the Association for Computational Linguistics - Hyatt Regency, Minneapolis, United States
Duration: 3 Jun 20195 Jun 2019
https://naacl2019.org

Conference

ConferenceAnnual Conference of the North American Chapter of the Association for Computational Linguistics
LocationHyatt Regency
LandUnited States
ByMinneapolis
Periode03/06/201905/06/2019
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