Bastiaan Bruinsma


2023

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Class Explanations: the Role of Domain-Specific Content and Stop Words
Denitsa Saynova | Bastiaan Bruinsma | Moa Johansson | Richard Johansson
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

We address two understudied areas related to explainability for neural text models. First, class explanations. What features are descriptive across a class, rather than explaining single input instances? Second, the type of features that are used for providing explanations. Does the explanation involve the statistical pattern of word usage or the presence of domain-specific content words? Here, we present a method to extract both class explanations and strategies to differentiate between two types of explanations – domain-specific signals or statistical variations in frequencies of common words. We demonstrate our method using a case study in which we analyse transcripts of political debates in the Swedish Riksdag.

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Sudden Semantic Shifts in Swedish NATO discourse
Brian Bonafilia | Bastiaan Bruinsma | Denitsa Saynova | Moa Johansson
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

In this paper, we investigate a type of semantic shift that occurs when a sudden event radically changes public opinion on a topic. Looking at Sweden’s decision to apply for NATO membership in 2022, we use word embeddings to study how the associations users on Twitter have regarding NATO evolve. We identify several changes that we successfully validate against real-world events. However, the low engagement of the public with the issue often made it challenging to distinguish true signals from noise. We thus find that domain knowledge and data selection are of prime importance when using word embeddings to study semantic shifts.