Unsupervised Part-of-speech Tagging

Evaluating Unsupervised Part-of-Speech Tagging for Grammar Induction

This paper explores the relationship between various measures of unsupervised part-of-speech tag induction and the performance of both supervised and unsupervised parsing models trained on induced tags. We find that no standard tagging metrics correlate well with unsupervised parsing performance, and several metrics grounded in information theory have no strong relationship with even supervised...

Unsupervised Part-Of-Speech Tagging Supporting Supervised Methods

This paper investigates the utility of an unsupervised partof-speech (PoS) system in a task oriented way. We use PoS labels as features for different supervised NLP tasks: Word Sense Disambiguation, Named Entity Recognition and Chunking. Further we explore, how much supervised tagging can gain from unsupervised tagging. A comparative evaluation between variants of systems using standard PoS, un...

Unsupervised Morphology Induction for Part-of-speech-tagging

Online Updating of Word Representations for Part-of-Speech Tagging

We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS) tagging evaluation, we find that online unsupervised DA performs as well as batch DA.

Weakly Supervised Part-of-Speech Tagging for Morphologically-Rich, Resource-Scarce Languages

This paper examines unsupervised approaches to part-of-speech (POS) tagging for morphologically-rich, resource-scarce languages, with an emphasis on Goldwater and Griffiths’s (2007) fully-Bayesian approach originally developed for English POS tagging. We argue that existing unsupervised POS taggers unrealistically assume as input a perfect POS lexicon, and consequently, we propose a weakly supe...