Two Decades of Unsupervised POS tagging---How Far Have We Come?

Part-of-speech (POS) induction is one of the most popular tasks in research on unsupervised NLP. Many different methods have been proposed, yet comparisons are difficult to make since there is little consensus on evaluation framework, and many papers evaluate against only one or two competitor systems. Here we evaluate seven different POS induction systems spanning nearly 20 years of work, using a variety of measures. We show that some of the oldest (and simplest) systems stand up surprisingly well against more recent approaches. Since most of these systems were developed and tested using data from the WSJ corpus, we compare their generalization abilities by testing on both WSJ and the multilingual Multext-East corpus. Finally, we introduce the idea of evaluating systems based on their ability to produce cluster prototypes that are useful as input to a prototype-driven learner. In most cases, the prototype-driven learner outperforms the unsupervised system used to initialize it, yielding state-of-the-art results on WSJ and improvements on non-English corpora.