Aarsleff, H. (1981). Bréal, la sémantique et Saussure.Histoire Épistémologie Langage, 3(2):115–133. (on p. 18)
Allahyaria, M. and Kochuta, K. (2015). Ontolda: An ontology-based topic model for automatic topic labeling. InProceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). (on p. 61)
Allison, S. D., Heuser, R., Jockers, M. L., Moretti, F., and Witmore, M. (2011). Quanti- tative formalism: an experiment. Stanford Literary Lab. (on p. 65)
Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete.Wired magazine, 16(7):16–07. (on p. 67)
Armstrong, T. G., Moffat, A., Webber, W., and Zobel, J. (2009). Improvements that don’t add up: ad-hoc retrieval results since 1998. InProceedings of the 18th ACM conference on Information and knowledge management, pages 601–610. ACM. (on p. 49)
Assar, S., Boughzala, I., and Boydens, I. (2011). Back to practice, a decade of research in e-government. InPractical studies in e-government, pages 1–12. Springer. (on p. 88)
Baker, P., Gabrielatos, C., Khosravinik, M., Krzy˙zanowski, M., McEnery, T., and Wodak, R. (2008). A useful methodological synergy? combining critical discourse analysis and corpus linguistics to examine discourses of refugees and asylum seekers in the uk press.Discourse & Society, 19(3):273–306. (on pp. 28 and 29) Balkenende, J. et al. (2005). Etymologisch Woordenboek van het Nederlands. Ams-
terdam University Press. (on p. 17) 181
Baum, L. E. and Petrie, T. (1966). Statistical inference for probabilistic functions of finite state Markov chains.The annals of mathematical statistics, 37(6):1554–1563.
(on p. 45)
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. C.
Davis, Printer to the Royal Society of London. (on p. 44)
Berners-Lee, T. (1989). Information management: A proposal. CERN memo. (on p. 45)
Bingel, J. and Haider, T. (2014). Named entity tagging a very large unbalanced cor- pus: Training and evaluating NE classifiers. InLREC, pages 2578–2583. (on p. 48) Blank, A. (1997). Prinzipien des lexikalischen Bedeutungswandels am Beispiel der
romanischen Sprachen, volume 285. Walter de Gruyter. (on p. 22)
Blank, A. (1999). Why do new meanings occur? A cognitive typology of the motiva- tions for lexical semantic change. Historical semantics and cognition, (13):6. (on pp. 22, 101, and 102)
Blank, A. and Koch, P. (1999).Historical semantics and cognition, volume 13. Walter de Gruyter. (on p. 18)
Blanke, T., Bryant, M., and Hedges, M. (2012). Open source optical character recog- nition for historical research.Journal of Documentation, 68(5):659–683. (on pp. 90 and 92)
Bledsoe, W. W. and Browning, I. (1959).Pattern recognition and reading by machine.
PGEC. (on p. 44)
Blei, D. M. (2012). Probabilistic topic models.Commun. ACM, 55(4):77–84. (on p. 57) Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet Allocation.Journal of
Machine Learning Research, 3:993–1022. (on pp. 45, 53, and 58)
Box, G. E., Draper, N. R., et al. (1987). Empirical model-building and response sur- faces, volume 424. John Wiley. (on p. 89)
Boydens, I. (1999).Informatique, normes et temps. Bruylant, Bruxelles. (on p. 89)
Boydens, I. (2011). Strategic issues relating to data quality for e-government: learn- ing from an approach adopted in belgium. InPractical Studies in E-government, pages 113–130. Springer. (on p. 89)
Boydens, I. and van Hooland, S. (2011). Hermeneutics applied to the quality of em- pirical databases.Journal of documentation, 67(2):279–289. (on p. 88)
Brauer, R. and Fridlund, M. (2013). Historicizing topic models, a distant reading of topic modeling texts within historical studies. InInternational Conference on Cultural Research in the context of “Digital Humanities”, St. Petersburg: Russian State Herzen University. (on p. 61)
Bréal, M. (1883). Les lois intellectuelles du langage : fragment de sémantique. In Annuaire de l’Association pour l’encouragement des études grecques en France, vol- ume 17, pages 132–142. (on p. 13)
Bréal, M. (1897).Essai de Sémantique. Hachette. (on pp. xi and 14)
Briscoe, E. J. (1988). Computational lexicography for natural language. Halsted Press. (on p. 25)
Brody, S. and Lapata, M. (2009). Bayesian word sense induction. InProceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pages 103–111. Association for Computational Linguistics. (on p. 53) Brooks, C. (1947). The well wrought urn: Studies in the structure of poetry. London:
Methuen. (on p. 65)
Buck, C. D. (1949).A dictionary of selected synonyms in the principal Indo-European languages. University of Chicago Press. (on p. 21)
Busa, R. (2008). Foreword. InA companion to digital humanitiesby Susan Schreib- man, Ray Siemens and John Unsworth. John Wiley & Sons. (on p. 62)
Cassin, B. (2016). Eloge de la traduction. Fayard, Paris. (on pp. 37 and 46)
Cavallin, K. (2012). Automatic extraction of potential examples of semantic change using lexical sets. InKONVENS, pages 370–377. (on pp. 32, 33, 35, 36, 158, and 164)
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., and Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. InAdvances in neural information processing systems, pages 288–296. (on pp. 59 and 61)
Chardonnens, A. and Hengchen, S. (2017). Text mining for user query analysis.
InEverything Changes, Everything Stays the Same? Understanding Information Spaces. Proceedings of the 15th International Symposium of Information Science (ISI 2017)-(isi2017). Humboldt-Universität zu Berlin. (on p. 88)
Chase, D., Trapasso, E., and Tolliver, R. (2016). The perfect storm: Examining user experience and conducting a usability test to investigate a disruptive academic library web site redevelopment. Journal of Web Librarianship, 10(1):28–44. (on p. 88)
Chomsky, N. (1956). Three models for the description of language. Information Theory, IRE Transactions on, 2(3):113–124. (on p. 45)
Chowdhury, G. (2010).Introduction to modern information retrieval. Facet publish- ing. (on p. 46)
Cleary, B., Brito, I. L., Huang, K., Gevers, D., Shea, T., Young, S., and Alm, E. J.
(2015). Detection of low-abundance bacterial strains in metagenomic datasets by eigengenome partitioning.Nature biotechnology, 33(10):1053–1060. (on p. 84) Coe, J. (2001).The Rotters’ Club. Viking Press. (on p. 49)
Cook, B. A. (2002).Belgium: A history, volume 50. Peter Lang. (on p. 121)
Cox, R. T. (1946). Probability, frequency and reasonable expectation.American jour- nal of physics, 14(1):1–13. (on p. 58)
Cremona, J. (1959). Historical semantics and the classification of semantic changes.
Hispanic Studies in Honour of I. González Llubera, ed. Francis W. Pierce (Oxford:
Dolphin, 1959), pages 1–6. (on p. 18)
Crymble, A. (2013). Review of Paper Machines, produced by Chris Johnson- Roberson and Jo Guldi. (on p. 82)
de Lhoneux, M., Shao, Y., Basirat, A., Kiperwasser, E., Stymne, S., Goldberg, Y., and Nivre, J. (2017). From raw text to universal dependencies-look, no tags! Pro- ceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 207–217. (on p. 30)
De Saussure, F. (1989). Cours de linguistique générale: Édition critique, volume 1.
Otto Harrassowitz Verlag. (on pp. 18 and 20)
De Wilde, M. (2015).From Information Extraction to Knowledge Discovery: Semantic Enrichment of Multilingual Content with Linked Open Data. PhD thesis, Univer- sité libre de Bruxelles. (on pp. 48, 63, 64, 65, 87, and 92)
De Wilde, M. and Hengchen, S. (2017). Semantic enrichment of a multilingual archive with linked open data.Digital Humanities Quarterly. (on p. 65)
Deerwester, S. C., Dumais, S. T., Furnas, G. W., Harshman, R. A., Landauer, T. K., Lochbaum, K. E., and Streeter, L. A. (1989). Computer information retrieval using latent semantic structure. US Patent 4,839,853. (on p. 53)
Dubossarsky, H., Shalit, U., Grossman, E., and Weinshall, D. (2015a). Using topic modeling to detect and quantify semantic change. New Developments in the Quantitative Study of Languages, page 15. (on p. 34)
Dubossarsky, H., Tsvetkov, Y., Dyer, C., and Grossman, E. (2015b). A bottom up ap- proach to category mapping and meaning change. InNetWordS, pages 66–70. (on p. 34)
Dubossarsky, H., Weinshall, D., and Grossman, E. (2017). Outta control: Laws of semantic change and inherent biases in word representation models. InProceed- ings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1147–1156. (on pp. 34 and 104)
Duguid, P. (2007). Inheritance and loss? A brief survey of Google Books. First Mon- day, 12(8). (on p. 93)
Durant, W. (1961). Story of philosophy. Simon and Schuster. (on p. 65)
Dury, P. and Picton, A. (2009). Terminologie et diachronie: vers une réconcilia- tion théorique et méthodologique? Revue française de linguistique appliquée, 14(2):31–41. (on p. 25)
Ehrmann, M. (2008).Les entités nommées, de la linguistique au TAL: statut théorique et méthodes de désambiguïsation. PhD thesis, Université Paris Diderot – Paris VII.
(on p. 48)
Enderle, J. S., Balagopalan, A., Li, X., and Newman, D. (2016). senderle/topic- modeling-tool: First stable release senderle/topic-modeling-tool: First stable re- lease. (on p. 76)
Falk, I., Bernhard, D., and Gérard, C. (2014). De la quenelle culinaire à la quenelle politique: identification de changements sémantiques à l’aide des topic models.
In21ème conférence sur le Traitement Automatique des Langues Naturelles, page 443. (on pp. 31, 32, and 36)
Farinosi, M., Lim, C., and Roll, J. (2016). Book or screen, pen or keyboard? A cross- cultural sociological analysis of writing and reading habits basing on Germany, Italy and the UK.Telematics and Informatics, 33(2):410–421. (on p. 48)
Feng, Y. and Lapata, M. (2010). Topic models for image annotation and text illus- tration. InHuman Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 831–839. Association for Computational Linguistics. (on p. 53)
Firth, J. R. (1957). A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis. (on pp. 24 and 51)
Fish, S. (2012). Mind your p’s and b’s: The digital humanities and interpretation.
New York Times, 23(1). (on p. 67)
Frandsen, P. J. (1990). Editing Reality: The Turin Strike Papyrus. InStudies in Egyp- tology Presented to Miriam Lichtheim, volume 1, pages 166–99. (on p. 121) Frermann, L. and Lapata, M. (2016). A bayesian model of diachronic meaning
change. Transactions of the Association for Computational Linguistics, 4:31–45.
(on pp. 34 and 36)
Geeraerts, D. (1997). Diachronic prototype semantics: A contribution to historical lexicology. Oxford University Press. (on pp. 17 and 18)
Goldberg, Y. (2016). A primer on neural network models for natural language pro- cessing.Journal of Artificial Intelligence Research, 57:345–420. (on p. 50)
Goldberg, Y. (2017). Neural network methods for natural language processing. Syn- thesis Lectures on Human Language Technologies, 10(1):1–309. (on p. 50)
Grant, M. (1999). Towards a central office of information: Continuity and change in British government information policy, 1939–51.Journal of Contemporary history, 34(1):49–67. (on p. 153)
Greene, D., O’Callaghan, D., and Cunningham, P. (2014). How many topics? Stabil- ity analysis for topic models. InMachine Learning and Knowledge Discovery in Databases, pages 498–513. Springer. (on pp. 61 and 101)
Griffiths, D. and Tenenbaum, M. (2004). Hierarchical topic models and the nested Chinese restaurant process. Advances in neural information processing systems, 16:17. (on p. 58)
Gulordava, K. and Baroni, M. (2011). A distributional similarity approach to the de- tection of semantic change in the Google Books ngram corpus. InProceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, pages 67–71. Association for Computational Linguistics. (on pp. 26 and 34) Haghighi, A. and Vanderwende, L. (2009). Exploring content models for multi-
document summarization. InProceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Com- putational Linguistics, pages 362–370. Association for Computational Linguistics.
(on p. 53)
Hamilton, W. L., Leskovec, J., and Jurafsky, D. (2016). Diachronic word embeddings reveal statistical laws of semantic change. arXiv preprint arXiv:1605.09096. (on pp. 33, 34, 35, 36, 50, 102, 104, and 168)
Hengchen, S., Coeckelbergs, M., van Hooland, S., Verborgh, R., and Steiner, T.
(2016a). Exploring archives with probabilistic models: Topic modelling for the
valorisation of digitised archives of the European Commission. InFirst Workshop Computational Archival Science: digital records in the age of big data, Washington, volume 8. (on pp. 100, 101, and 142)
Hengchen, S. and Koolen, M. (2015). topic-modeling-tool-fr: Working prototype.
(on p. 76)
Hengchen, S., O’Connor, A., Munnelly, G., and Edmond, J. (2016b). Comparing topic model stability across language and size. InProceedings of the Japanese Associa- tion for Digital Humanities Conference 2016. (on pp. 82, 84, and 148)
Hjelmslev, L. (1953).Prolegomena to a Theory of Language. University of Wisconsin Press. (on p. 19)
Hoffman, M., Bach, F. R., and Blei, D. M. (2010). Online learning for Latent Dirichlet Allocation. Inadvances in neural information processing systems, pages 856–864.
(on p. 84)
Hofmann, T. (1999). Probabilistic latent semantic indexing. InProceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 50–57. ACM. (on p. 56)
Hulpus, I., Hayes, C., Karnstedt, M., and Greene, D. (2013). Unsupervised graph- based topic labelling using DBpedia. InProceedings of the sixth ACM international conference on Web search and data mining, pages 465–474. ACM. (on p. 61) Jatowt, A. and Duh, K. (2014). A framework for analyzing semantic change of words
across time. InProceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, pages 229–238. IEEE Press. (on pp. 28, 29, and 153)
Jenset, G. B. and McGillivray, B. (2017).Quantitative Historical Linguistics: A Corpus Framework, volume 26. Oxford University Press. (on p. 86)
Joachims, T. (2002).Learning to classify text using support vector machines: Methods, theory and algorithms. Kluwer Academic Publishers. (on pp. 53, 60, and 100) Jockers, M. L. (2013).Macroanalysis: Digital methods and literary history. University
of Illinois Press. (on p. 61)
Jockers, M. L. and Mimno, D. (2013). Significant themes in 19th-century literature.
Poetics, 41(6):750–769. (on p. 61)
Jordanous, A. and Keller, B. (2016). Modelling creativity: identifying key components through a corpus-based approach.PloS one, 11(10):e0162959. (on pp. 30 and 32) Jurafsky, D. and Martin, J. H. (2000). Speech and language processing.International
Edition. (on p. 41)
Juran, J. (1951). Quality control handbook.McGraw-Hill. (on p. 87)
Koster, T. (2015). OCR improvement: helping and hindering researchers. (on p. 90) Kulkarni, V., Al-Rfou, R., Perozzi, B., and Skiena, S. (2015). Statistically significant
detection of linguistic change. InProceedings of the 24th International Conference on World Wide Web, pages 625–635. International World Wide Web Conferences Steering Committee. (on p. 29)
Landauer, T. K., Foltz, P. W., and Laham, D. (1998). An introduction to latent semantic analysis.Discourse processes, 25(2-3):259–284. (on p. 54)
Lau, J. H., Cook, P., McCarthy, D., Gella, S., and Baldwin, T. (2014a). Learning word sense distributions, detecting unattested senses and identifying novel senses us- ing topic models. InACL (1), pages 259–270. (on p. 31)
Lau, J. H., Cook, P., McCarthy, D., Newman, D., and Baldwin, T. (2012). Word sense induction for novel sense detection. InProceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 591–
601. Association for Computational Linguistics. (on pp. 31, 32, 36, and 167) Lau, J. H., Grieser, K., Newman, D., and Baldwin, T. (2011). Automatic labelling
of topic models. InProceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 1536–1545. Association for Computational Linguistics. (on pp. 53, 60, and 61) Lau, J. H., Newman, D., and Baldwin, T. (2014b). Machine reading tea leaves: Au-
tomatically evaluating topic coherence and topic model quality. InEACL, pages 530–539. (on p. 61)
Lau, J. H., Newman, D., Karimi, S., and Baldwin, T. (2010). Best topic word selection for topic labelling. InProceedings of the 23rd International Conference on Compu- tational Linguistics: Posters, pages 605–613. Association for Computational Lin- guistics. (on p. 60)
Lehrer, A. (1974).Semanticfields and lexical structure. American Elsevier. (on p. 20) Lehrer, A. (1985). The influence of semanticfields on semantic change. Historical semantics. Historical word-formation. Berlin: Mouton, pages 283–296. (on pp. 20 and 21)
Leibniz, G. (1684). Nova methodus pro maximis et minimis. Acta Eruditorum. (on p. 46)
Liberman, M. (2012). The ‘dance of the p’s and b’s’: truth or noise? Language log.
(on p. 67)
Liu, A. (2013). The meaning of the digital humanities.pmla, 128(2). (on p. 63) Loper, E. and Bird, S. (2002). NLTK: The natural language toolkit. InProceedings
of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics-Volume 1, pages 63–70. Asso- ciation for Computational Linguistics. (on p. 111)
Luhn, H. P. (1957). A statistical approach to mechanized encoding and searching of literary information.IBM Journal of research and development, 1(4):309–317. (on p. 56)
Magué, J.-P. (2005). Changements sémantiques et cognition: Différentes méthodes pour différentes échelles temporelles. PhD thesis, Université Lumière-Lyon II. (on pp. 12, 13, 14, 16, 19, and 21)
Manning, C. D. (2016). Computational linguistics and deep learning. Computa- tional Linguistics. (on p. 37)
McCallum, A. K. (2002a). Importing data. http://mallet.cs.umass.edu. (on p. 74) McCallum, A. K. (2002b). MALLET: A machine learning for language toolkit.
http://mallet.cs.umass.edu. (on p. 71)
McGillivray, B. (2013). Methods in Latin computational linguistics. Brill. (on p. 86) METAe (2004). Analyzed Layout and Text Object. Standard, METAe, Hamburg. (on
p. 108)
Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., Pickett, J. P., Hoiberg, D., Clancy, D., Norvig, P., and Orwant, J. (2011). Quantitative analysis of culture using millions of digitized books.science, 331(6014):176–182. (on pp. 24 and 45) Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013a). Distributed
representations of words and phrases and their compositionality. InAdvances in neural information processing systems, pages 3111–3119. (on pp. 46 and 50) Mikolov, T., Yih, W.-t., and Zweig, G. (2013b). Linguistic regularities in continuous
space word representations. InHlt-naacl, volume 13, pages 746–751. (on pp. 50 and 51)
Miller, J. H. (2001). Derrida and literature. In Cohen, T., editor,Jacques Derrida and the Humanities: A Critical Reader, pages 58–81. Cambridge University Press. (on p. 64)
Miltsakaki, E., Prasad, R., Joshi, A. K., and Webber, B. L. (2004). The Penn Discourse Treebank. InLREC. (on p. 45)
Mimno, D. (2013). mallet: A wrapper around the Java machine learning tool MAL- LET. (on p. 71)
Moens, M.-F. (2006).Information extraction: algorithms and prospects in a retrieval context, volume 21. Springer Science & Business Media. (on p. 46)
Moretti, F. (2005).Graphs, Maps, Trees: Abstract Models for a Literary History. Verso.
(on p. 64)
Morstatter, F., Pfeffer, J., Liu, H., and Carley, K. M. (2013). Is the sample good enough? Comparing data from twitter’s streaming API with twitter’sfirehose.
CoRR, abs/1306.5204. (on p. 82)
Mosteller, F. and Wallace, D. L. (1963). Inference in an authorship problem:
A comparative study of discrimination methods applied to the authorship of
the disputed federalist papers. Journal of the American Statistical Association, 58(302):275–309. (on p. 44)
Munnelly, G. (2015). elltm. https://github.com/munnellg/elltm. (on p. 84) Munnelly, G., O’Connor, A., Edmond, J., and Lawless, S. (2015). Finding meaning in
the chaos. (on p. 54)
Navigli, R. (2009). Word sense disambiguation: A survey. ACM Computing Surveys (CSUR), 41(2):10. (on p. 25)
Nerlich, B. (1992). Semantic Theories in Europe, 1830 1930: From etymology to con- textuality, volume 59. John Benjamins Publishing. (on pp. 11, 12, and 18)
Newman, D., Noh, Y., Talley, E., Karimi, S., and Baldwin, T. (2010). Evaluating topic models for digital libraries. InProceedings of the 10th annual joint conference on Digital libraries, pages 215–224. ACM. (on pp. 49 and 100)
Nivre, J., de Marneffe, M.-C., Ginter, F., Goldberg, Y., Hajic, J., Manning, C. D., Mc- Donald, R. T., Petrov, S., Pyysalo, S., Silveira, N., et al. (2016). Universal dependen- cies v1: A multilingual treebank collection. InLREC. (on p. 43)
Nyrop, K. (1914).Grammaire historique de la langue française, volume 1. Gyldendal.
(on p. 22)
Och, F. J. and Ney, H. (2003). A systematic comparison of various statistical align- ment models.Computational linguistics, 29(1):19–51. (on p. 45)
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002). Bleu: a method for auto- matic evaluation of machine translation. InProceedings of the 40th annual meet- ing on association for computational linguistics, pages 311–318. Association for Computational Linguistics. (on p. 26)
Pechenick, E. A., Danforth, C. M., and Dodds, P. S. (2015). Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolu- tion.PloS one, 10(10):e0137041. (on p. 35)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau,
D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learn- ing in Python.Journal of Machine Learning Research, 12:2825–2830. (on p. 85) Pettersson, E. (2016).Spelling normalisation and linguistic analysis of historical text
for information extraction. PhD thesis, Acta Universitatis Upsaliensis. (on pp. 112 and 166)
Philippa, M., Debrabandere, F., and Quak, A. (2005).Etymologisch Woordenboek van het Nederlands. Amsterdam University Press. (on p. 143)
Picton, A. (2009). Diachrony in Terminology. Defining a Tool-based Linguistic Methodology to Trace Knowledge Evolution in Specialized Corpora: A Practical Ex- ample from the Field of Space.PhD thesis, Université Toulouse le Mirail - Toulouse II. Co-financée par le Centre National d’Études Spatiales et le Centre National de la Recherche Scientifiques (Bourse de Docteur Ingénieur CNRS/Entreprise). (on p. 25)
Piotrowski, M. (2012). Natural language processing for historical texts. Synthesis Lectures on Human Language Technologies, 5(2):1–157. (on p. 86)
Pustejovsky, J., Hanks, P., Sauri, R., See, A., Gaizauskas, R., Setzer, A., Radev, D., Sund- heim, B., Day, D., Ferro, L., et al. (2003). The timebank corpus. InCorpus linguis- tics, volume 2003, page 40. (on p. 45)
R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. (on p. 71)
Radicati (2015). Email Statistics Report, 2015-2019. Technical report, The Radicati Group, Inc. (on p. 49)
Radicati, S. and Hoang, Q. (2012). Email Statistics Report, 2012-2016. Technical report, The Radicati Group, Inc. (on p. 49)
Ramage, D., Hall, D., Nallapati, R., and Manning, C. D. (2009). Labeled lda: A super- vised topic model for credit attribution in multi-labeled corpora. InProceedings
of the 2009 Conference on Empirical Methods in Natural Language Processing: Vol- ume 1-Volume 1, pages 248–256. Association for Computational Linguistics. (on p. 85)
Redman, T. C. (1997). Data quality for the information age. Artech House, Inc. (on p. 87)
Reh˚ˇ uˇrek, R. and Sojka, P. (2010). Software Framework for Topic Modelling with Large Corpora. InProceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 45–50, Valletta, Malta. ELRA. http://is.muni.cz/
publication/884893/en. (on p. 82)
Reisig, K. (1839). Vorlesungen über lateinische Sprachwissenschaft. Lehnhold. (on p. 12)
Reynaert, M. (2004). Text induced spelling correction. InProceedings of the 20th international conference on Computational Linguistics, page 834. Association for Computational Linguistics. (on p. 111)
Richards, I. A. (1929).Practical criticism, volume 4. Harcourt Brace Jovanovich, New York. (on p. 64)
Riddell, A., Hopper, T., and Grivas, A. (2016). lda: 1.0.4.
https://doi.org/10.5281/zenodo.57927. (on p. 85)
Riloff, E. and Lorenzen, J. (1999). Extraction-based text categorization: Generating domain-specific role relationships automatically. InNatural language informa- tion retrieval, pages 167–196. Springer. (on p. 46)
Rosen-Zvi, M., Griffiths, T., Steyvers, M., and Smyth, P. (2004). The author-topic model for authors and documents. InProceedings of the 20th conference on Un- certainty in artificial intelligence, pages 487–494. AUAI Press. (on p. 85)
Russell, S. and Norvig, P. (1995). Artifical Intelligence: A modern approach, vol- ume 25. Prentice Hall. (on p. 41)
Sagi, E., Kaufmann, S., and Clark, B. (2011). Tracing semantic change with latent semantic analysis. Current methods in historical semantics, pages 161–183. (on pp. 24, 25, 26, 28, 31, 33, and 34)
Salton, G., Wong, A., and Yang, C.-S. (1975). A vector space model for automatic indexing.Communications of the ACM, 18(11):613–620. (on p. 51)
Sellen, A. J. and Harper, R. H. (2003).The myth of the paperless office. MIT press. (on p. 48)
Sievert, C. and Shirley, K. E. (2014). Ldavis: A method for visualizing and interpreting topics. InProceedings of the workshop on interactive language learning, visualiza- tion, and interfaces, pages 63–70. (on pp. 73 and 74)
Sijs, N. v. d. (1996). Leenwoordenboek. De invloed van andere talen op het Neder- lands. Den Haag: SDU. (on p. 108)
Stern, G. (1931). Meaning and change of meaning; with special reference to the En- glish language.Wettergren & Kerbers. (on pp. 21 and 153)
Stern, R. (2013). Identification automatique d’entités pour l’enrichissement de con- tenus textuels. PhD thesis, Université Paris-Diderot-Paris VII. (on p. 48)
Suominen, A. and Toivanen, H. (2015). Map of science with topic modeling: Com- parison of unsupervised learning and human-assigned subject classification.
Journal of the Association for Information Science and Technology. (on p. 122) Svensson, P. (2013). Humanities computing as digital humanities. Defining Digital
Humanities: A Reader, pages 159–86. (on p. 62)
Swanson, D. R. (1986). Fish oil, Raynaud’s syndrome, and undiscovered public knowledge.Perspectives in biology and medicine, 30(1):7–18. (on p. 56)
Teh, Y. W., Jordan, M. I., Beal, M. J., and Blei, D. M. (2006). Hierarchical Dirichlet Processes. Journal of the American Statistical Association, 101(476):1566–1581.
(on p. 167)
Theissen, S. (2006). Duitse woorden en wendingen in Noord en Zuid. een onder- zoek ahv de zoekmachine google. Verslagen & Mededelingen van de Koninklijke Academie voor Nederlandse Taal-en Letterkunde, 116:129–146. (on p. 108)
Titov, I. and McDonald, R. (2008). Modeling online reviews with multi-grain topic models. InProceedings of the 17th international conference on World Wide Web, pages 111–120. ACM. (on p. 53)
Traugott, E. C. and Dasher, R. B. (2002). Regularity in semantic change, volume 96 of Cambridge Studies in Linguistics. (on p. 21)
Trier, J. (1931).Der deutsche Wortschatz im Sinnbezirk des Verstandes: die Geschichte eines sprachlichen Feldes. 1. Von den Anfängen bis zum Beginn des 13. Jahrhun- derts. Winter. (on p. 19)
Turing, A. M. (1948). Intelligent machinery, a heretical theory. The Turing Test: Ver- bal Behavior as the Hallmark of Intelligence, 105. (on p. 41)
Ullmann, S. (1959).The principles of semantics. Blackwell. (on pp. 14 and 18) Valéry, P. (1898). Compte rendu de Bréal (1897). Revue critique d’Histoire et de Lit-
terature, 45:141–43. (on p. 18)
Van Dale, J. H. (2005).Groot woordenboek der Nederlandse taal. Van Dale Uitgevers, Utrecht/Antwerpen. (on p. 130)
van Hooland, S., Gillet, F., Hengchen, S., and De Wilde, M. (2016). Introduction aux humanités numériques: méthodes et pratiques. De Boeck supérieur. (on pp. 66 and 76)
van Miltenburg, E. (2016). Dutch-tagger. https://github.com/evanmiltenburg/
Dutch-tagger. (on p. 111)
Wang, X. and McCallum, A. (2006). Topics over time: a non-Markov continuous-time model of topical trends. InProceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 424–433. ACM. (on p. 27)
Westeel, I. and Moufflet, J.-F. (2011). La conduite d’un projet de numérisation. in Manuel de la numérisation, Éditions du Cercle de la Librarie. (on p. 88)
Wiener, L. R. (1994). Les avatars du logiciel. Paris, Éditions Addison-Wesley France.
(on p. 89)
Wijaya, D. T. and Yeniterzi, R. (2011). Understanding semantic change of words over centuries. InProceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, pages 35–40. ACM. (on p. 27) Zavalina, O. and Vassilieva, E. V. (2014). Understanding the information needs of
large-scale digital library users. Library Resources & Technical Services, 58(2):84–
99. (on p. 88)
Zeng, J. (2012). TMBP.http://mloss.org/software/view/399/. (on p. 85)
