Learning Language (with) Grammars: From Teaching Latin to Learning Domain-Specific Grammars
Abstract
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: 10.1145/3078081.3078108 Paper 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, 2019 Paper 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/W18-3715 Paper 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: 10.3233/978-1-61499-904-1-61 Herbert 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
Degree
Doctor of Philosophy
University
Göteborgs universitet. IT-fakulteten
Institution
Department of Computer Science and Engineering ; Institutionen för data- och informationsteknik
Disputation
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
2020-09-16
herbert.lange@cse.gu.se
Date
2020-08-25Author
Lange, Herbert
Keywords
Latin
Latin syntax
Latin morphology
Grammar engineering
Grammar testing
Corpus-based evaluation
Computer-assisted language learning
Grammar learning
Constraint satisfaction
Constraint optimization
Publication type
Doctoral thesis
ISBN
978-91-7833-987-7 (PDF)
978-91-7833-986-0 (Print)
Series/Report no.
185D
Language
eng
Metadata
Show full item recordRelated items
Showing items related by title, author, creator and subject.
-
Heuristisk analys med Diderichsens satsschema. Tillämpningar för svensk text
Wilhelmsson, Kenneth (2010-03-26)A heuristic method for parsing Swedish text, heuristic schema parsing, is described and im-plemented. Focusing on main clause (primary) analysis, a collection of licensing techniques for removing non-primary verb candidates ... -
En matchningsdriven semantisk modell. Mellan ordboken och den interna grammatiken
Rydstedt, Rudolf (2012-05-08)This thesis presents Matchningsdriven semantisk modell [Match-driven Semantic Model] (MSM). The Model was developed in order to facilitate the analysis of the semantic structure of corpus data, but the central component, ... -
Initiala annex i en teckenbaserad konstruktionsgrammatik
Strandberg, Viktoria (2019-02-12)Left-dislocations (Sw. initial dislokation ’initial dislocation’, ID) and hanging scenes (Sw. fritt initial annex som motsvarar ett fritt adverbial i den inre satsen ’free initial annex corresponding to a free adverbial ...