Statistical parsers are effective but are typically limited to producing projective dependencies or constituents. On the other hand, linguistically rich parsers recognize non-local relations and analyze both form and function phenomena but rely on extensive manual grammar engineering. We combine advantages of the two by building a statistical parser that produces richer analyses. We investigate new techniques to implement treebank-based parsers that allow for discontinuous constituents. We present two systems. One system is based on a Linear Context-Free Rewriting System (lcfrs), while using a Probabilistic Discontinuous Tree-Substitution Grammar (pdtsg) to improve disambiguation performance. Another system encodes discontinuities in the labels of phrase-structure trees, allowing for efficient context-free grammar parsing. The two systems demonstrate that tree fragments as used in treesubstitution grammar improve disambiguation performance while capturing non-local relations on an as-needed basis. Additionally, we present results for models that produce function tags, resulting in a more linguistically adequate model of the data. We report substantial accuracy improvements in discontinuous parsing for German, English, and Dutch, including results on spoken Dutch. This article is a substantially revised and extended version of van Cranenburgh and Bod (2013). While finishing this article, we learned with great sadness of the passing of our co-author Remko Scha. We dedicate this article to his memory.

Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results.

We present a simple but effective method for aspect identification in sentiment analysis. Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages. We introduce Contrastive Attention (CAt ), a novel single-head attention mechanism based on an RBF kernel, which gives a considerable boost in performance and makes the model interpretable. Previous work relied on syntactic features and complex neural models. We show that given the simplicity of current benchmark datasets for aspect extraction, such complex models are not needed. The code to reproduce the experiments reported in this paper is available at https://github.com/clips/cat.

Peeking into the inner workings of BERT has shown that its layers resemble the classical NLP pipeline, with progressively more complex tasks being concentrated in later layers.To investigate to what extent these results also hold for a language other than English, we probe a Dutch BERT-based model and the multilingual BERT model for Dutch NLP tasks. In addition, through a deeper analysis of partof-speech tagging, we show that also within a given task, information is spread over different parts of the network and the pipeline might not be as neat as it seems. Each layer has different specialisations, so that it may be more useful to combine information from different layers, instead of selecting a single one based on the best overall performance.