AMALGAM: Automatic Mapping Among Lexico-Grammatical Annotation Models


The title of this paper playfully contrasts two rather different approaches to language analysis. The "Noisy Channel" 's are the promoters of statistically based approaches to language learning. Many of these studies are based on the Shannons's Noisy Channel model. The "Braying Donkey" 's are those oriented towards theoretically motivated language models. They are interested in any type of language expressions (such as the famous "Donkey Sentences"), regardless of their frequency in real language, because the focus is the study of human communication. In the past few years, we supported a more balanced approach. While our major concern is applicability to real NLP systems, we think that, after all, quantitative methods in Computational Linguistic should provide not only practical tools for language processing, but also some linguistic insight. Since, for sake of space, in this paper we cannot give any complete account of our research, we will present examples of "linguistically appealing", automatically acquired, lexical data (selectional restrictions of words) obtained trough an integrated use of knowledge-based and statistical techniques. We discuss the pros and cons of adding symbolic knowledge to the corpus linguistic recipe.