Unsupervised Discovery of Phonological Categories through Supervised Learning of Morphological Rules

We describe a case study in the application of {\em symbolic machine learning} techniques for the discovery of linguistic rules and categories. A supervised rule induction algorithm is used to learn to predict the correct diminutive suffix given the phonological representation of Dutch nouns. The system produces rules which are comparable to rules proposed by linguists. Furthermore, in the process of learning this morphological task, the phonemes used are grouped into phonologically relevant categories. We discuss the relevance of our method for linguistics and language technology

Penalizing unknown words’ emissions in hmm pos tagger based on Malay affix morphemes

The challenge in unsupervised Hidden Markov Model (HMM) training for a POS tagger isthat the training depends on an untagged corpus; the only supervised data limiting  possible tagging of words is a dictionary. Therefore, training cannot properly map  possible tags. The exact morphemes of prefixes, suffixes and circumfixes in the   agglutinative Malay language is examined to assign unknown words’ probable tags based on linguistically meaningful affixes using a morpheme-based POS guessing algorithm for tagging. The algorithm has been integrated into Viterbi algorithm which uses HMM trained parameters for tagging new sentences. In the experiment, this tagger is first, uses character-based prediction to handle unknown words; next, uses morpheme-based POS guessing algorithm; lastly, combination of the first and second.Keywords: Malay POS tagger; morpheme-based; HMM

Entry Generation for New Words by Analogy for Morphological Lexicons

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Implicit learning of recursive context-free grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

Corpus-based paradigm Selection for morphological entries

Volume: 4 Host publication title: Nealt Proceedings Series Vol. 4 Host publication sub-title: Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009Peer reviewe