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Combining part of speech induction and morphological induction

Linguistic information is useful in natural language processing, information retrieval and a multitude of sub-tasks involving language analysis. Two types of linguistic information in all languages are part of speech and morphology. Part of speech information reflects syntactic structure and can assist in tasks such as speech recognition, machine translation and word sense disambiguation. Morphological information describes the structure of words and has application in automated spelling correction, natural language generation and information retrieval for morphologically complex languages. Machine learning methods in natural language processing acquire linguistic information from corpora of natural language text. While supervised learning algorithms are trained on texts that have been annotated with linguistic features, induction algorithms learn linguistic information from unannotated corpora. Such algorithms avoid any requirement for linguistically annotated training data a resource that is highly time-intensive to produce. However, in learning from unannotated corpora, only limited sources of information are available. In practice, part of speech induction methods usually learn from distributional evidence about the contexts in which words occur. In contrast, morphological induction methods tend to be based on the orthographic structure of the words in the corpus. However, a word’s morphological form and syntactic function often correlate: a word’s morphology may indicate its syntactic function and vice versa. Thus, both distributional and orthographic evidence may be useful for both tasks. This thesis investigates the extent to which the information induced by one learner can be used to bootstrap the other: specifically, whether the incorporation of explicit annotations from one learner can improve the performance of the other.