Modelling the Lexicon in Unsupervised Part of Speech Induction
Table 2: M-1 Accuracy of Lex-HMM and PYP-HMM models: Comparison of M-1 accuracy for the lexicon based model (Lex-HMM) and the PYP-HMM model on several languages. The Lex-HMM and PYP-HMM columns indicate the results of word type based particle filtering with 10 and 100 particles, respectively, while the Local and 1HMM columns use the token based sampler and the 1HMM approximation described by Blunsom and Cohn blunsom11acl. The token based sampler was run for 500 iterations and the other samplers for 200. The percentages in brakets represent the average accuracy over all languages except for Hungarian.
['Language', 'Lex-HMM', 'PYP-HMM', 'Local', '1HMM', 'Lex-HMM+LM', 'PYP-HMM+LM', '1HMM+LM']
These models both ignore word morphology and rely on word order. The 1HMM approximation achieves the highest average accuracy. The Lex-HMM model matches or surpasses the type-based PYP-HMM approach in six languages while running much faster due to the particle filter considering a smaller set of parts-of-speech for each particle. However, in the absence of morphological information, the Lex-HMM model has a similar average accuracy to the local and type-based PYP-HMM samplers. The especially low performance on Hungarian, a language with free word ordering and strong morphology, suggests that the Lex-HMM model struggles to find ambiguity classes without morphology. The Lex-HMM model has a higher average accuracy than the type-based or local PYP-HMM samplers when Hungarian is ignored. The language model leads to consistently improved performance for each of the samplers excepting the token sampler, which is unable to mix properly with the additional complexity. The accuracies achieved by the 1HMM+LM sampler represent the previous state-of-the-art. These results show that the Lex-HMM+LM model achieves state-of-the-art M-1 accuracies on several datasets, including the English WSJ. The Lex-HMM+LM model performs nearly as well as, and often better than, the 1HMM+LM sampler without any restrictions on tag assignments.