MSc thesis: Reconstructing language ancestry by performing word prediction
I am working on a MSc thesis, in which I apply machine learning methods to the field of historical linguistics, looking at how languages are ancestrally related. By predicting words in one language from words in another language, several tasks in historical linguistics can be performed, such as phylogenetic tree reconstruction, identification of sound correspondences and cognate detection. Currently, I am applying recurrent neural networks for the prediction task. The thesis is supervised by Gerhard Jäger (SfS, University of Tübingen) and Jelle Zuidema (ILLC, University of Amsterdam).
- Talk at the workshop Phylogenetic Methods in Historical Linguistics, Tübingen (March 27-30, 2017) [pdf]
Agent-based modelling of change in use of the genitive
I developed an agent model of historical change of the genitive in Germanic languages. Data from the Icelandic saga corpus was used, to initialize agents with an Old Norse language model, representing an ancestor of current Scandinavian languages. In some experiments, language contact between Scandinavian and Middle German agents was simulated. Simulations tend to converge to situations observed in current Germanic languages.
This was a course project, in which I collaborated with other students, for the MA course Variation and Change in German/Variation and Change in Scandinavian Languages at the University of Amsterdam, taught by prof. dr. Arjen Versloot.
- Paper “Modelling change in use of the genitive: an agent-based approach” (unpublished) [pdf]
- Source code [link]
TREC OpenSearch: information retrieval evaluation
I am maintaining the TREC OpenSearch API for the ILPS research group of the UvA. TREC OpenSearch is a project which enables information retrieval researchers to evaluate their search result rankings on users of real websites. Site administrators are in turn able to use technology based on the latest research.
- TREC OpenSearch
- ILPS research group
- Overview paper:
BSc thesis: Determining Dutch dialect phylogeny using bayesian inference
In my BSc thesis, I explored a topic in dialectometry: using bayesian inference to create a kinship tree of Dutch dialects. I used data from the Reeks Nederlandse Dialectatlassen. The words were aligned and converted to phonetic features, in order to be processed by a bayesian inference algorithm. The resulting tree was compared to an existing Dutch dialect map. The thesis was supervised by Alexis Dimitriadis and Martin Everaert from UiL OTS.