A novel approach to part-of-speech tagging based on latent analogy

Part-of-speech tagging is a necessary pre-processing step for many natural language tasks. Recent statistical approaches, such as conditional random fields, rely on well chosen feature functions to ensure that important characteristics of the empirical training distribution are reflected in the trained model. In practice, however, it is not always clear how to best select these feature functions in order to obtain a suitably robust model. This paper proposes an alternative strategy based on the principle of latent analogy. For each sentence under consideration, we construct a neighborhood of globally relevant training sentences through an appropriate data-driven mapping of the input surface form. Tagging then proceeds via locally optimal sequence alignment and maximum likelihood position scoring. Empirical evidence shows that this solution is competitive with state-of-the-art Markovian techniques.