An efficient memory-based morphosyntactic tagger and parser for Dutch

By Antal van den Bosch, Bertjan Busser, S. Canisius, Walter Daelemans. 2007

231 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Generalization through Memorization: Nearest Neighbor Language Models (2019, methodology): “…neighbor models have been applied to a number of NLP problems in the past, such as part of speech tagging (Daelemans et al., 1996) and morphological analysis (Bosch et al., 2007), but the use of learned representations makes the similarity function much more effective in the case of neural models.”
The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms (2021, methodology): “For the automated text analyses, all headlines were preprocessed by lemmatizing them using the Dutch lemmatizer Frog (Van den Bosch et al., 2007).”
Deriving de/het gender classification for Dutch nouns for rule-based MT generation tasks (2014, methodology): “…gender information; Section 4 presents evaluation results for Precision and coverage for different frequency thresholds; Section 4 gives interpretation of the results; Section 6 discusses the development context, generalisation of our methodology for rule-based MT and some ideas for future work.”
Case-based Reasoning for Natural Language Queries over Knowledge Bases (2021, unknown): “, 1996) and morphological analysis (Bosch et al., 2007).”