Unsupervised Part-of-Speech Induction

TL;DR

This paper presents an unsupervised method for inducing part-of-speech tags using hierarchical clustering, offering a viable alternative when labeled data is unavailable, and demonstrates promising results compared to existing methods.

Contribution

It introduces a hierarchical agglomerative clustering approach for unsupervised POS induction, improving upon prior unsupervised tagging techniques.

Findings

Achieved promising accuracy in POS induction

Outperformed some existing unsupervised POS taggers

Demonstrated effectiveness without labeled data

Abstract

Part-of-Speech (POS) tagging is an old and fundamental task in natural language processing. While supervised POS taggers have shown promising accuracy, it is not always feasible to use supervised methods due to lack of labeled data. In this project, we attempt to unsurprisingly induce POS tags by iteratively looking for a recurring pattern of words through a hierarchical agglomerative clustering process. Our approach shows promising results when compared to the tagging results of the state-of-the-art unsupervised POS taggers.