PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improved Unsupervised POS Induction through Prototype Discovery
Omri Abend, Roi Reichart and Ari Rappoport
ACL 2010 2010.

Abstract

We present a novel fully unsupervised algorithm for POS induction from plain text, motivated by the cognitive notion of prototypes. The algorithm first identifies landmark clusters of words, serving as the cores of the induced POS categories. The rest of the words are subsequently mapped to these clusters. We utilize morphological and distributional representations computed in a fully unsupervised manner. We evaluate our algorithm on English and German, achieving the best reported results for this task.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7065
Deposited By:Ari Rappoport
Deposited On:27 February 2011