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A.W. (Andreas) van Cranenburgh, PhD

Assistant professor Digital Humanities and Information Sciences

A.W. (Andreas) van Cranenburgh, PhD
E-mail:
a.w.van.cranenburgh rug.nl

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  1. 2021
  2. Luoto, S., & van Cranenburgh, A. (2021). Psycholinguistic dataset on language use in 1145 novels published in English and Dutch. Data in brief, 34, [106655]. https://doi.org/10.1016/j.dib.2020.106655
  3. 2020
  4. Koolen, C., van Dalen-Oskam, K., Cranenburgh, van, A., & Nagelhout, E. (2020). Literary quality in the eye of the Dutch reader: The National Reader Survey. Poetics, 79, [101439]. https://doi.org/10.1016/j.poetic.2020.101439
  5. Poot, C., & van Cranenburgh, A. (2020). A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News. In M. Ogrodniczuk, V. Ng, Y. Grishina, & S. Pradhan (Eds.), Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference (pp. 79-90). Association for Computational Linguistics (ACL).
  6. Cranenburgh, van, A. (2020). An Empirical Evaluation of Sentiment Analysis on Movie Scripts. Paper presented at DH Benelux 2020 - Online, . https://doi.org/10.5281/zenodo.3862158
  7. Tulkens, S., & van Cranenburgh, A. (2020). Embarrassingly Simple Unsupervised Aspect Extraction. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 3182-3187). ACL. https://www.aclweb.org/anthology/2020.acl-main.290/
  8. van Cranenburgh, A., & Koolen, C. (2020). Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments. In Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 121-126). International Committee on Computational Linguistics.
  9. de Vries, W., van Cranenburgh, A., & Nissim, M. (2020). What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 4339-4350). Association for Computational Linguistics (ACL).
  10. 2019
  11. de Vries, W., van Cranenburgh, A., Bisazza, A., Caselli, T., Noord, van, G., & Nissim, M. (2019). BERTje: A Dutch BERT Model. ArXiv, [1912.09582]. https://arxiv.org/abs/1912.09582
  12. Cranenburgh, van, A. (2019). A Dutch coreference resolution system with an evaluation on literary fiction. Computational Linguistics in the Netherlands Journal, 9.
  13. van Cranenburgh, A., van Dalen-Oskam, K., & van Zundert, J. (2019). Vector space explorations of literary language. Language Resources and Evaluation, 53(4), 625-650. https://doi.org/10.1007/s10579-018-09442-4
  14. van Cranenburgh, A. (2019). A Dutch coreference resolution system with quote attribution. Poster session presented at Computational Linguistics in the Netherlands 29 (CLIN29), Groningen, Netherlands.
  15. van Cranenburgh, A., & Koolen, C. (2019). The Literary Pepsi Challenge: intrinsic and extrinsic factors in judging literary quality. In Proceedings of Digital Humanities 2019 conference Utrecht University. https://dev.clariah.nl/files/dh2019/boa/0557.html
  16. 2018
  17. Bladier, T., van Cranenburgh, A., Evang, K., Kallmeyer, L., Möllemann, R., & Osswald, R. (2018). RRGbank: a Role and Reference Grammar Corpus of Syntactic Structures Extracted from the Penn Treebank. In Proceedings of the 17th International Workshop on Treebanks and Linguistic Theories
  18. Bladier, T., van Cranenburgh, A., & Kallmeyer, L. (2018). Extraction of LTAG-based supertags from the French Treebank: challenges and possible solutions.
  19. Bladier, T., van Cranenburgh, A., Evang, K., Kallmeyer, L., Möllemann, R., & Osswald, R. (2018). RRGbank—creating a Role and Reference Grammar resource through an automatic conversion of the Penn treebank.
  20. van Cranenburgh, A. (2018). Active DOP: A constituency treebank annotation tool with online learning. In D. Zhao (Ed.), Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations Association for Computational Linguistics (ACL).
  21. Bladier, T., van Cranenburgh, A., Samih, Y., & Kallmeyer, L. (2018). German and French Neural Supertagging Experiments for LTAG Parsing. In V. Shwartz, J. Tabassum, R. Voigt, W. Che, M-C. de Marneffe, & M. Nissim (Eds.), Proceedings of ACL 2018, Student Research Workshop Association for Computational Linguistics (ACL).
  22. Koolen, C., & van Cranenburgh, A. (2018). Blue eyes and porcelain cheeks: Computational extraction of physical descriptions from Dutch chick lit and literary novels. Digital Scholarship in the Humanities, 33(1), 59-71. https://doi.org/10.1093/llc/fqx016
  23. van Cranenburgh, A. (2018). Cliche expressions in literary and genre novels. In Proceedings of Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
  24. 2017
  25. van Cranenburgh, A., & Bod, R. (2017). A Data-Oriented Model of Literary Language. In Proceedings of EACL (pp. 1228-1238). Association for Computational Linguistics (ACL). http://aclweb.org/anthology/E17-1115
  26. Koolen, C., & van Cranenburgh, A. (2017). These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution. In Proceedings of the First Ethics in NLP workshop (pp. 12-22). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/W17-1602
  27. 2016
  28. van Cranenburgh, A. (2016). Rich Statistical Parsing and Literary Language. Institute for Logic, Language and Computation. http://andreasvc.github.io/phdthesis_v1.1.pdf
  29. van Cranenburgh, A., Scha, R., & Bod, R. (2016). Data-Oriented Parsing with discontinuous constituents and function tags. Journal of Language Modelling, 4(1), 57-111. https://doi.org/10.15398/jlm.v4i1.100
  30. van Cranenburgh, A. (2016). Machine Learning Literature using Textual Features. Tiny Transactions on Computer Science, 4.
  31. Jautze, K., van Cranenburgh, A., & Koolen, C. (2016). Topic Modeling Literary Quality. In Digital Humanities 2016: Conference Abstracts (pp. 233-237). http://dh2016.adho.org/abstracts/95
  32. 2015
  33. van Cranenburgh, A., & Koolen, C. (2015). Identifying literary texts with bigrams. In Proceedings of CLFL (pp. 58-67). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/W15-0707
  34. Sangati, F., & van Cranenburgh, A. (2015). Multiword expression identification with recurring tree fragments and association measures. In Proceedings of the 11th Workshop on Multiword Expressions (pp. 10-18). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/W15-0902
  35. 2014
  36. van Cranenburgh, A. (2014). Extraction of phrase-structure fragments with a linear average time tree kernel. Computational Linguistics in the Netherlands Journal, 4, 3-16. https://clinjournal.org/clinj/article/view/36
  37. Roorda, D., Kalkman, G., Naaijer, M., & van Cranenburgh, A. (2014). LAF-Fabric: A data analysis tool for linguistic annotation framework with an application to the Hebrew Bible. Computational Linguistics in the Netherlands Journal, 4, 105-120.
  38. 2013
  39. van Cranenburgh, A., & Bod, R. (2013). Discontinuous parsing with an efficient and accurate DOP model. In Proceedings of IWPT (pp. 7-16). Association for Computational Linguistics (ACL). http://aclweb.org/anthology/W13-5701
  40. Jautze, K., Koolen, C., van Cranenburgh, A., & de Jong, H. (2013). From high heels to weed attics: A syntactic investigation of chick lit and literature. In Proceedings of Computational Linguistics for Literature workshop (pp. 72-81). Association for Computational Linguistics (ACL). http://aclweb.org/anthology/W13-1410
  41. 2012
  42. van Cranenburgh, A. (2012). Literary authorship attribution with phrase-structure fragments. In Proceedings of the NAACL-HLT 2012 Workshop on Computational Linguistics for Literature (pp. 59-63). Association for Computational Linguistics (ACL). http://aclweb.org/anthology/W12-2508
  43. Aloni, M., van Cranenburgh, A., Fernández, R., & Sznajder, M. (2012). Building a Corpus of Indefinite Uses Annotated with Fine-grained Semantic Functions. In The eighth international conference on Language Resources and Evaluation (LREC) (pp. 1511-1515). European Language Resources Association (ELRA). https://www.aclweb.org/anthology/L12-1178/
  44. van Cranenburgh, A. (2012). Efficient parsing with linear context-free rewriting systems. In Proceedings of EACL (pp. 460-470). Association for Computational Linguistics (ACL). http://aclweb.org/anthology/E12-1047
  45. 2011
  46. van Cranenburgh, A., Scha, R., & Sangati, F. (2011). Discontinuous Data-Oriented Parsing: A mildly context-sensitive all-fragments grammar. In oceedings of the Second Workshop on Statistical Parsing of Morphologically Rich Languages (pp. 34-44). Association for Computational Linguistics (ACL). http://aclweb.org/anthology/W11-3805
  47. 2010
  48. van Cranenburgh, A., Sassoon, G., & Fernández, R. (2010). Invented antonyms: Esperanto as a semantic lab. In Proceedings of the 26th Annual Meeting of the Israel Association for Theoretical Linguistics (IATL 26) (pp. 1-8). Ramat Gan, Israel: Bar-Ilan University. http://dare.uva.nl/en/record/371912

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