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

TL;DR

This paper introduces an online unsupervised domain adaptation method for POS tagging that updates word representations incrementally, matching the performance of traditional batch methods without needing large data batches.

Contribution

It presents a novel online unsupervised domain adaptation approach for POS tagging, enabling incremental updates without batch processing.

Findings

Online unsupervised DA matches batch DA performance in POS tagging

Incremental updates are effective without large data batches

Method is applicable in real-time or streaming data scenarios

Abstract

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.