Inferring inflection classes with description length

International audienceWe discuss the notion of an inflection class system, a traditional ingredient of the description of inflection systems of nontrivial complexity. We distinguish systems of microclasses, which partition a set of lexemes in classes with identical behavior, and systems of macroclasses, which group lexemes that are similar enough in a few larger classes. On the basis of the intuition that macroclasses should contribute to a concise description of the system, we propose one algorithmic method for inferring macroclasses from raw inflectional paradigms, based on minimisation of the description length of the system under a given strategy of identifying morphological alternations in paradigms. We then exhibit classifications produced by our implementation on French and European Portuguese conjugation data and argue that they constitute an appropriate systematisation of traditional classifications. To arrive at such a convincing systematisation, it was crucial for us to use a local approach to inflection class similarity (based on pairwise comparisons of paradigm cells) rather than a global approach (based on the simultaneous comparison of all cells). We conclude that it is indeed possible to infer inflectional macroclasses objectively

Unsupervised Induction of Natural Language Morphology Inflection Classes

We propose a novel language-independent framework for inducing a collection of morphological inflection classes from a monolingual corpus of full form words. Our approach involves two main stages. In the first stage, we generate a large data structure of candidate inflection classes and their interrelationships. In the second stage, search and filtering techniques are applied to this data structure, to identify a select collection of "true " inflection classes of the language. We describe the basic methodology involved in both stages of our approach and present an evaluation of our baseline techniques applied to induction of major inflection classes of Spanish. The preliminary results on an initial training corpus already surpass an F1 of 0.5 against ideal Spanish inflectional morphology classes.

Unsupervised Induction of Natural Language Morphology Inflection Classes

We propose a novel language-independent framework for inducing a collection of morphological inflection classes from a monolingual corpus of full form words.  Our approach involves two main stages.  In the first stage, we generate a large data structure of candidate inflection classes and their interrelationships.  In the second stage, search and filtering techniques are applied to this data structure, to identify a select collection of "true" inflection classes of the language.  We describe the basic methodology involved in both stages of our approach and present an evaluation of our baseline techniques applied to induction of major inflection classes of Spanish.  The preliminary results on an initial training corpus already surpass an F1 of 0.5 against ideal Spanish inflectional morphology classes.</p

Unsupervised Induction of Natural Language Morphology Inflection Classes

We propose a novel language-independent framework for inducing a collection of morphological inflection classes from a monolingual corpus of full form words. Our approach involves two main stages. In the first stage, we generate a large data structure of candidate inflection classes and their interrelationships. In the second stage, search and filtering techniques are applied to this data structure, to identify a select collection of "true" inflection classes of the language. We describe the basic methodology involved in both stages of our approach and present an evaluation of our baseline techniques applied to induction of major inflection classes of Spanish. The preliminary results on an initial training corpus already surpass an F1 of 0.5 against ideal Spanish inflectional morphology classes