Quantifying the morphosyntactic content of Brown Clusters

Manuel Ciosici, Leon Derczynski, Ira Assent

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics
Vol/bind1
ForlagAssociation for Computational Linguistics
Publikationsdatojun. 2019
Sider1541–1550
ArtikelnummerN19-1157
ISBN (Trykt)978-1-950737-13-0
StatusUdgivet - jun. 2019

Fingeraftryk

Dyk ned i forskningsemnerne om 'Quantifying the morphosyntactic content of Brown Clusters'. Sammen danner de et unikt fingeraftryk.

Citationsformater