Henrik Björklund


pdf bib
Semi-Supervised Topic Modeling for Gender Bias Discovery in English and Swedish
Hannah Devinney | Jenny Björklund | Henrik Björklund
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing

Gender bias has been identified in many models for Natural Language Processing, stemming from implicit biases in the text corpora used to train the models. Such corpora are too large to closely analyze for biased or stereotypical content. Thus, we argue for a combination of quantitative and qualitative methods, where the quantitative part produces a view of the data of a size suitable for qualitative analysis. We investigate the usefulness of semi-supervised topic modeling for the detection and analysis of gender bias in three corpora (mainstream news articles in English and Swedish, and LGBTQ+ web content in English). We compare differences in topic models for three gender categories (masculine, feminine, and nonbinary or neutral) in each corpus. We find that in all corpora, genders are treated differently and that these differences tend to correspond to hegemonic ideas of gender.


pdf bib
Parsing Weighted Order-Preserving Hyperedge Replacement Grammars
Henrik Björklund | Frank Drewes | Petter Ericson
Proceedings of the 16th Meeting on the Mathematics of Language


pdf bib
Single-Rooted DAGs in Regular DAG Languages: Parikh Image and Path Languages
Martin Berglund | Henrik Björklund | Frank Drewes
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

pdf bib
Predicting User Competence from Linguistic Data
Yonas Woldemariam | Henrik Björklund | Suna Bensch
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)


pdf bib
On the Parameterized Complexity of Linear Context-Free Rewriting Systems
Martin Berglund | Henrik Björklund | Frank Drewes
Proceedings of the 13th Meeting on the Mathematics of Language (MoL 13)