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In this paper we present work in developing a computerized grammar for the Latin language. It demonstrates the principles and challenges in developing a grammar for a natural language in a modern grammar formalism. The grammar presented here provides a useful resource for natural language processing applications in different fields. It can be easily adopted for language learning and use in language technology for Cultural Heritage like translation applications or to support post-correction of document digitization.
MULLE is a tool for language learning that focuses on teaching Latin as a foreign language. It is aimed for easy integration into the traditional classroom setting and syllabus, which makes it distinct from other language learning tools that provide standalone learning experience. It uses grammar-based lessons and embraces methods of gamification to improve the learner motivation. The main type of exercise provided by our application is to practice translation, but it is also possible to shift the focus to vocabulary or morphology training.
We present a language learning application that relies on grammars to model the learning outcome. Based on this concept we can provide a powerful framework for language learning exercises with an intuitive user interface and a high reliability. Currently the application aims to augment existing language classes and support students by improving the learner attitude and the general learning outcome. Extensions beyond that scope are promising and likely to be added in the future.
This thesis describes work in three areas: grammar engineering, computer-assisted language learning and grammar learning. These three parts are connected by the concept of a grammar-based language learning application. Two types of grammars are of concern. The first we call resource grammars, extensive descriptions a natural languages. Part I focuses on this kind of grammars. The other are domain-specific or application-specific grammars. These grammars only describe a fragment of natural language that is determined by the domain of a certain application. Domain-specific grammars are relevant for Part II and Part III. Another important distinction is between humans learning a new natural language using computational grammars (Part II) and computers learning grammars from example sentences (Part III). Part I of this thesis focuses on grammar engineering and grammar testing. It describes the development and evaluation of a computational resource grammar for Latin. Latin is known for its rich morphology and free word order, both have to be handled in a computationally efficient way. A special focus is on methods how computational grammars can be evaluated using corpus data. Such an evaluation is presented for the Latin resource grammar. Part II, the central part, describes a computer-assisted language learning application based on domain-specific grammars. The language learning application demonstrates how computational grammars can be used to guide the user input and how language learning exercises can be modeled as grammars. This allows us to put computational grammars in the center of the design of language learning exercises used to help humans learn new languages. Part III, the final part, is dedicated to a method to learn domain- or application-specific grammars based on a wide-coverage grammar and small sets of example sentences. Here a computer is learning a grammar for a fragment of a natural language from example sentences, potentially without any additional human intervention. These learned grammars can be based e.g. on the Latin resource grammar described in Part II and used as domain-specific lesson grammars in the language learning application described Part II.
In this paper we investigate the problem of grammar inference from a different perspective. The common approach is to try to infer a grammar directly from example sentences, which either requires a large training set or suffers from bad accuracy. We instead view it as a problem of grammar restriction or sub-grammar extraction. We start from a large-scale resource grammar and a small number of examples, and find a sub-grammar that still covers all the examples. To do this we formulate the problem as a constraint satisfaction problem, and use an existing constraint solver to find the optimal grammar. We have made experiments with English, Finnish, German, Swedish and Spanish, which show that 10–20 examples are often sufficient to learn an interesting domain grammar. Possible applications include computer-assisted language learning, domain-specific dialogue systems, computer games, Q/A-systems, and others.