Task-specific Word-Clustering for Part-of-Speech Tagging

2012/05/19 by Yoav Goldberg, Goldberg, Yoav
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1205.4298

openalex publication_date 2012/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

While the use of cluster features became ubiquitous in core NLP tasks, most cluster features in NLP are based on distributional similarity. We propose a new type of clustering criteria, specific to the task of part-of-speech tagging. Instead of distributional similarity, these clusters are based on the beha vior of a baseline tagger when applied to a large corpus. These cluster features provide similar gains in accuracy to those achieved by distributional-similarity derived clusters. Using both types of cluster features together further improve tagging accuracies. We show that the method is effective for both the in-domain and out-of-domain scenarios for English, and for French, German and Italian. The effect is larger for out-of-domain text.

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