Martin Kroon


2024

pdf bib
A Canonical Form for Flexible Multiword Expressions
Jan Odijk | Martin Kroon
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper proposes a canonical form for Multiword Expressions (MWEs), in particular for the Dutch language. The canonical form can be enriched with all kinds of annotations that can be used to describe the properties of the MWE and its components. It also introduces the DUCAME (DUtch CAnonical Multiword Expressions) lexical resource with more than 11k MWEs in canonical form. DUCAME is used in MWE-Finder to automatically generate queries for searching for flexible MWEs in large text corpora.

pdf bib
MWE-Finder: A Demonstration
Jan Odijk | Martin Kroon | Tijmen Baarda | Ben Bonfil | Sheean Spoel
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper introduces and demonstrates MWE Finder, an application to search for flexible multiword expressions (MWEs) in Dutch text corpora, starting from an example. If the example is in canonical form, the application automatically generates three queries to search for sentences that contain an occurrence of the MWE and thus enables efficient analysis of its properties. Searching is done in treebanks, so the grammatical structure of the sentences is taken into account.

2018

pdf bib
When Simple n-gram Models Outperform Syntactic Approaches: Discriminating between Dutch and Flemish
Martin Kroon | Masha Medvedeva | Barbara Plank
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

In this paper we present the results of our participation in the Discriminating between Dutch and Flemish in Subtitles VarDial 2018 shared task. We try techniques proven to work well for discriminating between language varieties as well as explore the potential of using syntactic features, i.e. hierarchical syntactic subtrees. We experiment with different combinations of features. Discriminating between these two languages turned out to be a very hard task, not only for a machine: human performance is only around 0.51 F1 score; our best system is still a simple Naive Bayes model with word unigrams and bigrams. The system achieved an F1 score (macro) of 0.62, which ranked us 4th in the shared task.

2017

pdf bib
When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages
Maria Medvedeva | Martin Kroon | Barbara Plank
Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

We present the results of our participation in the VarDial 4 shared task on discriminating closely related languages. Our submission includes simple traditional models using linear support vector machines (SVMs) and a neural network (NN). The main idea was to leverage language group information. We did so with a two-layer approach in the traditional model and a multi-task objective in the neural network case. Our results confirm earlier findings: simple traditional models outperform neural networks consistently for this task, at least given the amount of systems we could examine in the available time. Our two-layer linear SVM ranked 2nd in the shared task.