Learning Language (with) Grammars: From Teaching Latin to Learning Domain-Specific Grammars
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 appli- cation 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.
Parts of work
Paper 1: Herbert Lange: “Implementation of a Latin Grammar in Grammatical Framework”, Published in Proceedings of the 2 nd International Conference on Digital Access to Textual Cultural Heritage (DATeCH2017), Göttingen, Germany, 2017, DOI: 10.1145/3078081.3078108Paper 2: Herbert Lange: “An Open-Source Computational Latin Grammar: Overview and Evaluation”, Submitted to Proceedings of the 20 th International Colloquium on Latin Linguistics (ICLL 2019), Las Palmas de Gran Canaria, 2019Paper 3: Herbert Lange and Peter Ljunglöf: “MULLE: A Grammar-Based Latin Language Learning Tool to Supplement the Classroom Setting”, Published in Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA ’18), Melbourn. Australia, 2018, DOI: 10.18653/v1/W18-3715Paper 4: Herbert Lange and Peter Ljunglöf: “Putting Control into Language Learning”, Published in Proceedings of the 6th International Workshop on Controlled Natural Languages (CNL 2018), Maynooth, Ireland, 2018, DOI: 10.3233/978-1-61499-904-1-61Herbert Lange and Peter Ljunglöf: “Learning Domain-Specific Grammars From a Small Number of Examples”, Submitted to Special Issue: Natural Language Processing in Artificial Intelligence - NLPinAI 2020, in: Series “Studies in Computational Intelligence” (SCI), Springer
Doctor of Philosophy
Göteborgs universitet. IT-fakulteten
Department of Computer Science and Engineering ; Institutionen för data- och informationsteknik
Onsdagen den 16 september 2020, kl. 10.00, Rum 8103, EDIT Building, Hörsalsvägen 11 och online http://www.cse.chalmers.se/~langeh/defense.html
Date of defence
Computer-assisted language learning
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