A Tool for Language Learning Based on Categorial Grammars and Semantic Information

Abstract

Natural language learning still remains an open problem, although there exist many approaches issued by actual researches. We also address ourselves this challenge and we provide here a prototype of a tool. First we need to clarify that we center on the syntactic level. We intend to find a (set of) grammar(s) that recognizes new correct sentences (in the sense of the correct order of the words) by means of some initial correct examples that are presented and of a strategy to deduce the corresponding grammar(s) consistent(s) with the examples at each step. In this model, the grammars are the support of the languages, so, the process of learning is a process of grammatical inference. Usually, in NLP approaches, natural language is represented by lexicalized grammars because the power of the language consists in the information provided by the words and their combination schemas. That’s why we adopt here the formal model of a categorial grammar that assigns every word a category and furnishes some general combination schema of categories. But, in our model, the strings of words are not sufficient for the inference, so additional information is needed. In Kanazawa’s work [3] the additional information is the internal structure of each sentence as a Structural Example. We try to provide instead a more lexicalized information, of semantic nature: the semantic type of words. Its provenance, as well as the psycho-linguistic motivation can be found in [1] and [2].

There exist different classes of categorial grammars depending on the set of combination schemas used: classical categorial grammars, combinatory grammars, Lambek grammars.