1887
  1. Home
  2. e-Journals
  3. International Journal of Corpus Linguistics
  4. Online First Articles
  5. Online First Article
image of From unannotated to annotated
USD
Buy:$35.00 + Taxes

Abstract

The utility of historical language databases is hindered by their complexity, text variety, and lack of register information. We investigate the feasibility of deriving linguistically motivated and reliable register predictions for unannotated, long historical texts. We fine-tune BERT-based deep learning models using register-annotated data from the Corpus of Founding Era American English and predict registers for different text parts of unannotated Eighteenth Century English Online documents. We determine the model’s effectiveness in capturing pervasive linguistic features across different text sections (e.g. beginnings vs. endings), analyzing how document internal variation affects model performance and identifying which sections are most effectively predicted. Additionally, we employ the Stable Attribution Class Explanation method to extract and compare keywords from various text parts to determine the quality of the predictions. Our findings indicate that text beginnings consistently provide more reliable classifications.

© John Benjamins Publishing Company
Loading

Article metrics loading...

/content/journals/10.1075/ijcl.24137.rep
2026-06-10
2026-07-17

  • From This Site

    /content/journals/10.1075/ijcl.24137.rep
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Adesam, Y., Dannélls, D., & Tahmasebi, N.
    (2019) Exploring the quality of the digital historical newspaper archive KubHist. InC. Navarretta, M. Agirrezabal, & B. Maegaard (Eds.), Proceedings of the 4th conference of the association digital humanities in the Nordic countries (pp.–). CEUR Workshop Proceedings. https://ceur-ws.org/Vol-2364/1_paper.pdf. 10.5617/dhnbpub.11018
     https://doi.org/10.5617/dhnbpub.11018 [Google Scholar]
  2. Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M.
    (2019) Optuna: A next-generation hyperparameter optimization framework. InA. Teredesai & V. Kumar (Eds.), Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp.–). Association for Computing Machinery. 10.1145/3292500.3330701
     https://doi.org/10.1145/3292500.3330701 [Google Scholar]
  3. Alatrash, R., Schlechtweg, D., Kuhn, J., & Schulte in Walde, S.
    (2020) CCOHA: Clean Corpus of Historical American English. InN. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the twelfth Language Resources and Evaluation Conference (pp.–). European Language Resources Association. https://aclanthology.org/2020.lrec-1.859
     [Google Scholar]
  4. Baker, P.
    (2004) Querying keywords: Questions of difference, frequency, and sense in keywords analysis. Journal of English Linguistics, (), –. 10.1177/0075424204269894
     https://doi.org/10.1177/0075424204269894 [Google Scholar]
  5. Beck, C., & Köllner, M.
    (2023) GHisBERT — Training BERT from scratch for lexical semantic investigations across historical German language stages. InN. Tahmasebi, S. Montariol, H. Dubossarsky, A. Kutuzov, S. Hengchen, D. Alfter, F. Periti, & P. Cassotti (Eds.), Proceedings of the 4th workshop on computational approaches to historical language change (pp.–). Association for Computational Linguistics. 10.18653/v1/2023.lchange‑1.4
     https://doi.org/10.18653/v1/2023.lchange-1.4 [Google Scholar]
  6. Bergs, A.
    (2004) Letters: A new approach to text typology. Journal of Historical Pragmatics, (), –. 10.1075/jhp.5.2.04ber
     https://doi.org/10.1075/jhp.5.2.04ber [Google Scholar]
  7. Biber, D.
    (1988) Variation across speech and writing. Cambridge University Press. 10.1017/CBO9780511621024
     https://doi.org/10.1017/CBO9780511621024 [Google Scholar]
  8. (2001) Dimensions of variation among 18th century registers. InH.-J. Diller & M. Görlach (Eds.), Towards a history of English as a history of genres (pp.–). C. Winter.
     [Google Scholar]
  9. (2012) Register as a predictor of linguistic variation. Corpus Linguistics and Linguistic Theory, (), –. 10.1515/cllt‑2012‑0002
     https://doi.org/10.1515/cllt-2012-0002 [Google Scholar]
  10. Biber, D., & Conrad, S.
    (2019) Introduction: Multi-dimensional analysis and the study of register variation. InS. Conrad & D. Biber (Eds.), Variation in English: Multi-dimensional studies (pp.–). Routledge. 10.4324/9781315840888
     https://doi.org/10.4324/9781315840888 [Google Scholar]
  11. Biber, D., & Egbert, J.
    (2023) What is a register?: Accounting for linguistic and situational variation within — and outside of — textual varieties. Register Studies, (), –. 10.1075/rs.00004.bib
     https://doi.org/10.1075/rs.00004.bib [Google Scholar]
  12. Biber, D., & Finegan, E.
    (1989) Drift and the evolution of English style: A history of three genres. Language, (), –. 10.2307/415220
     https://doi.org/10.2307/415220 [Google Scholar]
  13. (1992) The linguistic evolution of five written and speech-based English genres from the 17th to the 20th centuries. InM. Rissanen, O. Ihalainen, T. Nevalainen, & I. Taavitsainen (Eds.), History of Englishes: New methods and interpretations in historical linguistics (pp.–). De Gruyter Mouton. 10.1515/9783110877007.688
     https://doi.org/10.1515/9783110877007.688 [Google Scholar]
  14. (1997) Diachronic relations among speech-based and written registers in English. InT. Nevalainen & L. Kahlas-Tarkka (Eds.), To explain the present: Studies in the changing English language in honour of Matti Rissanen (pp.–). Société Néophilologique.
     [Google Scholar]
  15. Biber, D., & Gray, B.
    (2013) Being specific about historical change: The influence of sub-register. Journal of English Linguistics, (), –. 10.1177/0075424212472509
     https://doi.org/10.1177/0075424212472509 [Google Scholar]
  16. Chiche, A., & Yitagesu, B.
    (2022) Part of speech tagging: A systematic review of deep learning and machine learning approaches. Journal of Big Data, (), . 10.1186/s40537‑022‑00561‑y
     https://doi.org/10.1186/s40537-022-00561-y [Google Scholar]
  17. Culpeper, J., & Kytö, M.
    (2010) Early Modern English dialogues: Spoken interaction as writing. Cambridge University Press.
     [Google Scholar]
  18. Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Quoc V. Le, & Salakhutdinov, R.
    (2019) Transformer-XL: Attentive language models beyond a fixed-length context. InA. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th annual meeting of the Association for Computational Linguistics (pp.–). Association for Computational Linguistics. 10.18653/v1/P19‑1285
     https://doi.org/10.18653/v1/P19-1285 [Google Scholar]
  19. Damian-Grint, P.
    (2013) Eighteenth-Century literature in English and other languages: Image, text, and hypertext. InR. Siemens, & S. Schreibman (Eds.), A Companion to digital literary studies (pp.–). Wiley. 10.1002/9781405177504.ch5
     https://doi.org/10.1002/9781405177504.ch5 [Google Scholar]
  20. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K.
    (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. InJ. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of NAACL HLT 2019 (pp.–). Association for Computational Linguistics. 10.18653/v1/N19‑1423
     https://doi.org/10.18653/v1/N19-1423 [Google Scholar]
  21. Egbert, J., & Biber, D.
    (2019) Incorporating text dispersion into keyword analyses. Corpora, (), –. 10.3366/cor.2019.0162
     https://doi.org/10.3366/cor.2019.0162 [Google Scholar]
  22. (2023) Key feature analysis: A simple, yet powerful method for comparing text varieties. Corpora, (), –. 10.3366/cor.2023.0275
     https://doi.org/10.3366/cor.2023.0275 [Google Scholar]
  23. Egbert, J., & Gracheva, M.
    (2023) Linguistic variation within registers: Granularity in textual units and situational parameters. Corpus Linguistics and Linguistic Theory, (), –. 10.1515/cllt‑2022‑0034
     https://doi.org/10.1515/cllt-2022-0034 [Google Scholar]
  24. Egbert, J., & Mahlberg, M.
    (2020) Fiction — one register or two?: Speech and narration in novels. Register Studies, (), –. 10.1075/rs.19006.egb
     https://doi.org/10.1075/rs.19006.egb [Google Scholar]
  25. Fanego, T., Rodríguez-Puente, P., López-Couso, M., Méndez-Naya, B., Núñez-Pertejo, P., Blanco-García, C., & Tamaredo, I.
    (2017) The Corpus of Historical English Law Reports 1535–1999 (CHELAR): A resource for analysing the development of English legal discourse. ICAME Journal, (), –. 10.1515/icame‑2017‑0003
     https://doi.org/10.1515/icame-2017-0003 [Google Scholar]
  26. Gabay, S., Ortiz Suarez, P., Bartz, A., Chagué, A., Bawden, R., Gambette, P., & Sagot, B.
    (2022) From FreEM to D’AlemBERT: A large corpus and a language model for early modern French. InN. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, J. Odijk, & S. Piperidis (Eds.), Proceedings of the thirteenth Language Resources and Evaluation Conference (pp.–). European Language Resources Association. https://aclanthology.org/2022.lrec-1.359. 10.63317/22guf3yfi2fk
     https://doi.org/10.63317/22guf3yfi2fk [Google Scholar]
  27. Geisler, C.
    (2002) Investigating register variation in nineteenth-century English: A multi-dimensional comparison. InR. Reppen, S. M. Fitzmaurice, & D. Biber (Eds.), Using corpora to explore linguistic variation (pp.–). John Benjamins. 10.1075/scl.9.17gei
     https://doi.org/10.1075/scl.9.17gei [Google Scholar]
  28. González-Álvarez, D. & Pérez-Guerra, J.
    (1998) Texting the written evidence: On register analysis in late Middle English and Early Modern English. Text & Talk, (), –. 10.1515/text.1.1998.18.3.321
     https://doi.org/10.1515/text.1.1998.18.3.321 [Google Scholar]
  29. Goulart, L., Biber, D., & Reppen, R.
    (2022) In this essay, I will …: Examining variation of communicative purpose in student written genres. Journal of English for Academic Purposes, , . 10.1016/j.jeap.2022.101159
     https://doi.org/10.1016/j.jeap.2022.101159 [Google Scholar]
  30. Gregg, S.
    (2020) Old books and digital publishing: Eighteenth-Century Collections Online. Cambridge University Press. 10.1017/9781108767415
     https://doi.org/10.1017/9781108767415 [Google Scholar]
  31. Gries, S.
    (2021) A new approach to (key) keywords analysis: Using frequency, and now also dispersion. Research in Corpus Linguistics, (), –. 10.32714/ricl.09.02.02
     https://doi.org/10.32714/ricl.09.02.02 [Google Scholar]
  32. Grieve, J., Biber, D., Friginal, E., & Nekrasova, T.
    (2011) Variation among blog text types: A multi-dimensional analysis. InA. Mehler, S. Sharoff, & M. Santini (Eds.), Genres on the Web: Corpus studies and computational models (pp.–). Springer. 10.1007/978‑90‑481‑9178‑9
     https://doi.org/10.1007/978-90-481-9178-9 [Google Scholar]
  33. Hashimoto, B.
    (2023) Corpus of Founding Era American English: Designing a corpus for interpreting the United States Constitution. Corpora, (), –. 10.3366/cor.2023.0270
     https://doi.org/10.3366/cor.2023.0270 [Google Scholar]
  34. Holahan, C.
    (2021) Rummaging in the dark: Ecco as opaque digital archive. Eighteenth-Century Studies, (), –. 10.1353/ecs.2021.0093
     https://doi.org/10.1353/ecs.2021.0093 [Google Scholar]
  35. Kyröläinen, A. J., & Laippala, V.
    (2023) Predictive keywords: Using machine learning to explain document characteristics. Frontiers in Artificial Intelligence, , . 10.3389/frai.2022.975729
     https://doi.org/10.3389/frai.2022.975729 [Google Scholar]
  36. Kytö, M.
    (2019) Register in historical linguistics. Register Studies, (), –. 10.1075/rs.18011.kyt
     https://doi.org/10.1075/rs.18011.kyt [Google Scholar]
  37. Laippala, V., Egbert, J., Biber, D., & Kyröläinen, A.-J.
    (2021) Exploring the role of lexis and grammar for the stable identification of register in an unrestricted corpus of web documents. Language Resources and Evaluation, , –. 10.1007/s10579‑020‑09519‑z
     https://doi.org/10.1007/s10579-020-09519-z [Google Scholar]
  38. Laippala, V., Rönnqvist, S., Oinonen, M., Kyröläinen, A.-J., Salmela, A., Biber, D., Egbert, J., & Pyysalo, S.
    (2023) Register identification from the unrestricted open web using the Corpus of Online Registers of English. Language Resources and Evaluation, (), –. 10.1007/s10579‑022‑09624‑1
     https://doi.org/10.1007/s10579-022-09624-1 [Google Scholar]
  39. Lehto, A.
    (2010) Complexity in national legislation of the Early Modern English period. Journal of Historical Pragmatics, (), –. 10.1075/jhp.11.2.05leh
     https://doi.org/10.1075/jhp.11.2.05leh [Google Scholar]
  40. Liimatta, A., Ryan, Y., Säily, T., & Tolonen, M.
    (2023) Results from rough data? The large-scale study of early modern historiography with multi-dimensional register analysis. Digital Humanities in the Nordic and Baltic Countries Publications, (), –. 10.5617/dhnbpub.10668
     https://doi.org/10.5617/dhnbpub.10668 [Google Scholar]
  41. Lyu, Z., Jin, Z., Gonzalez, F., Mihalcea, R., Schoelkopf, B., & Sachan, M.
    (2024) On the causal nature of sentiment analysis. arXiv. arxiv.org/abs/2404.11055
     [Google Scholar]
  42. Manjavacas, E., & Fonteyn, L.
    (2021) MacBERTh: Development and evaluation of a historically pre-trained language model for English (1450–1950). InM. Hämäläinen, K. Alnajjar, N. Partanen, & J. Rueter (Eds.), Proceedings of the workshop on natural language processing for digital humanities (pp.–). NLP Association of India (NLPAI). https://aclanthology.org/2021.nlp4dh-1.4
     [Google Scholar]
  43. (2022a) Adapting vs. pre-training language models for historical languages. Journal of Data Mining & Digital Humanities, . 10.46298/jdmdh.9152
     https://doi.org/10.46298/jdmdh.9152 [Google Scholar]
  44. (2022b) Non-parametric word sense disambiguation for historical languages. InM. Hämäläinen, K. Alnajjar, N. Partanen, & J. Rueter (Eds.), Proceedings of the 2nd international workshop on natural language processing for digital humanities, (pp.–). Association for Computational Linguistics. 10.18653/v1/2022.nlp4dh‑1.16
     https://doi.org/10.18653/v1/2022.nlp4dh-1.16 [Google Scholar]
  45. Monaco, L. M.
    (2017) A Multidimensional analysis of Late Modern English scientific texts from the Coruña Corpus [Doctoral dissertation, The University of A Coruña]. University of A Coruña Repository. https://www.udc.es/en/teses/tese/?codigo=n70
  46. Murphy, S.
    (2019) Shakespeare and his contemporaries: Designing a genre classification scheme for Early English Books Online 1560–1640. ICAME Journal, (), –. 10.2478/icame‑2019‑0003
     https://doi.org/10.2478/icame-2019-0003 [Google Scholar]
  47. Ozsarfati, E., Sahin, E., Saul, C. J., & Yilmaz, A.
    (2019) Book genre classification based on titles with comparative machine learning algorithms. InY. Xiao & N. Funabiki (Eds.), 2019 IEEE 4th international conference on computer and communication systems (pp.–). Institute of Electrical and Electronics Engineers. 10.1109/CCOMS.2019.8821643
     https://doi.org/10.1109/CCOMS.2019.8821643 [Google Scholar]
  48. Paquot, M., & Bestgen, Y.
    (2009) Distinctive words in academic writing: A comparison of three statistical tests for keyword extraction. InA. Jucker, D. Schreier, & M. Hundt (Eds.). Corpora: Pragmatics and discourse, (pp.–). Brill. 10.1163/9789042029101_014
     https://doi.org/10.1163/9789042029101_014 [Google Scholar]
  49. Petrenz, P., & Webber, B.
    (2011) Stable classification of text genres. Computational Linguistics, (), –. 10.1162/COLI_a_00052
     https://doi.org/10.1162/COLI_a_00052 [Google Scholar]
  50. Rastas, I., Ciarán Ryan, Y., Tiihonen, I., Qaraei, M., Repo, L., Babbar, R., Mäkelä, E., Tolonen, M., & Ginter, F.
    (2022) Explainable publication year prediction of eighteenth century texts with the BERT model. InN. Tahmasebi, S. Montariol, A. Kutuzov, S. Hengchen, H. Dubossarsky, & L. Borin (Eds.), Proceedings of the 3rd workshop on computational approaches to historical language change (pp.–). Association for Computational Linguistics. 10.18653/v1/2022.lchange‑1.7
     https://doi.org/10.18653/v1/2022.lchange-1.7 [Google Scholar]
  51. Rayson, P.
    (2008) From key words to key semantic domains. International Journal of Corpus Linguistics, (), –. 10.1075/ijcl.13.4.06ray
     https://doi.org/10.1075/ijcl.13.4.06ray [Google Scholar]
  52. Repo, L., Hashimoto, B., Liimatta, A., Saario, L., Säily, T., Tiihonen, I., Tolonen, M., & Laippala, V.
    (2024) Toward automatic register classification in unrestricted databases of historical English. InS. Coats & V. Laippala (Eds.), Linguistics across disciplinary borders: The march of data (pp.–). Bloomsbury. 10.5040/9781350362291.0011
     https://doi.org/10.5040/9781350362291.0011 [Google Scholar]
  53. Repo, L., Hashimoto, B., & Laippala, V.
    (2023) In search of founding era registers: Automatic modeling of registers from the Corpus of Founding Era American English. Digital Scholarship in the Humanities, (), –. 10.1093/llc/fqad049
     https://doi.org/10.1093/llc/fqad049 [Google Scholar]
  54. Rönnqvist, S., Kyröläinen, A.-J., Myntti, A., Ginter, F., & Laippala, V.
    (2022) Explaining classes through stable word attributions. InS. Muresan, P. Nakov, & A. Villavicencio (Eds.), Findings of the Association for Computational Linguistics (pp.–). Association for Computational Linguistics. 10.18653/v1/2022.findings‑acl.85
     https://doi.org/10.18653/v1/2022.findings-acl.85 [Google Scholar]
  55. Schweter, S., März, L., Schmid, K., & Çano, E.
    (2022) hmBERT: Historical multilingual language models for named entity recognition. InG. Faggioli, N. Ferro, A. Hanbury, & M. Potthast (Eds.), Proceedings of the working notes of CLEF 2022 — Conference and Labs of the Evaluation Forum (pp.–). Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-3180/paper-87.pdf
     [Google Scholar]
  56. Scott, M.
    (1997) PC analysis of key words — and key key words. System, (), –. 10.1016/S0346‑251X(97)00011‑0
     https://doi.org/10.1016/S0346-251X(97)00011-0 [Google Scholar]
  57. Sharoff, S.
    (2018) Functional text dimensions for the annotation of web corpora. Corpora, (), –. 10.3366/cor.2018.0136
     https://doi.org/10.3366/cor.2018.0136 [Google Scholar]
  58. Sundararajan, M., Taly, A., & Yan, Q.
    (2017) Axiomatic attribution for deep networks. Proceedings of Machine Learning Research, , –. https://proceedings.mlr.press/v70/sundararajan17a.html
     [Google Scholar]
  59. Swales, J. M.
    (1990) Genre analysis: English in academic and research settings. Cambridge University Press.
     [Google Scholar]
  60. Taavitsainen, I., Hiltunen, T., Smith, J., & Suhr, C.
    (Eds.) (2022) Genre in English medical writing, 1500–1820: Sociocultural contexts of production and use. Cambridge University Press. 10.1017/9781009105347
     https://doi.org/10.1017/9781009105347 [Google Scholar]
  61. Taavitsainen, I., & Schneider, G.
    (2019) Scholastic argumentation in early English medical writing and its afterlife: New corpus evidence. InC. Suhr, T. Nevalainen, & I. Taavitsainen (Eds.), From data to evidence in English language research (pp.–). Brill. 10.1163/9789004390652_010
     https://doi.org/10.1163/9789004390652_010 [Google Scholar]
  62. Tolonen, M., Mäkelä, E., Ijaz, A., & Lahti, L.
    (2021) Corpus linguistics and Eighteenth Century Collections Online (ECCO). Research in Corpus Linguistics, (), –. 10.32714/ricl.09.01.03
     https://doi.org/10.32714/ricl.09.01.03 [Google Scholar]
  63. Tsirmpas, D., Gkionis, I., Papadopoulos, G. T., & Mademlis, I.
    (2024) Neural natural language processing for long texts: A survey on classification and summarization. Engineering Applications of Artificial Intelligence, , . 10.1016/j.engappai.2024.108231
     https://doi.org/10.1016/j.engappai.2024.108231 [Google Scholar]
  64. Underwood, T., Black, M. L., Auvil, L., & Capitanu, B.
    (2013) Mapping mutable genres in structurally complex volumes. InT. Y. Lin, V. Raghavan, & B. Wah (Eds.), 2013 IEEE international conference on big data, (pp.–). Institute of Electrical and Electronics Engineers. 10.1109/BigData.2013.6691676
     https://doi.org/10.1109/BigData.2013.6691676 [Google Scholar]
  65. Vanetik, N., Tiamanova, M., Kogan, G., & Litvak, M.
    (2024) Genre classification of books in Russian with stylometric features: A case study. Information, (), . 10.3390/info15060340
     https://doi.org/10.3390/info15060340 [Google Scholar]
  66. van de Voorde, I., Rutten, G., Vosters, R., van der Wal, M., & Vandenbussche, W.
    (2023) Historical Corpus of Dutch: A new multi-genre corpus of Early and Late Modern Dutch. Taal en Tongval, (), –. 10.5117/TET2023.1.006.VAND
     https://doi.org/10.5117/TET2023.1.006.VAND [Google Scholar]
  67. van Strien, D.
    (2022) Can we use machine learning. to classify whether a book is ‘fiction’ or ‘non-fiction’ from its title?Living with Machines. https://livingwithmachines.ac.uk/can-we-use-machine-learning-to-classify-whether-a-book-is- fiction-or-non-fiction-from-its-title/
     [Google Scholar]
  68. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I.
    (2017) Attention is all you need. arXiv. 10.48550/arXiv.1706.03762
     https://doi.org/10.48550/arXiv.1706.03762 [Google Scholar]
  69. Worsham, J., & Kalita, J.
    (2018) Genre identification and the compositional effect of genre in literature. InE. M. Bender, L. Derczynski, & P. Isabelle (Eds.), Proceedings of the 27th international conference on computational linguistics (pp.–). Association for Computational Linguistics. https://aclanthology.org/C18-1167/
     [Google Scholar]
  70. Wright, S.
    (1994) The place of genre in the corpus. InM. Kytö, M. Rissanen, & S. Wright (Eds.), Corpora across the centuries: Proceedings of the first international colloquium on English diachronic corpora (pp.–). Brill. 10.1163/9789004657311_015
     https://doi.org/10.1163/9789004657311_015 [Google Scholar]
  71. Yang, S., Choi, M., Cho, Y., & Choo, J.
    (2023) HistRED: A historical document-level relation extraction dataset. InA. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st annual meeting of the Association for Computational Linguistics (pp.–). Association for Computational Linguistics. 10.18653/v1/2023.acl‑long.180
     https://doi.org/10.18653/v1/2023.acl-long.180 [Google Scholar]
  72. Zhang, J., Ryan, Y. C., Rastas, I., Ginter, F., Tolonen, M., & Babbar, R.
    (2022) Detecting sequential genre change in eighteenth-century texts. InF. Karsdorp, A. Lassche, & K. Nielbo (Eds.), Proceedings of the computational humanities research conference 2022 (pp.–). Computational Humanities Research. https://researchportal.bath.ac.uk/files/302636539/short_paper2630.pdf
     [Google Scholar]
/content/journals/10.1075/ijcl.24137.rep
Loading
From unannotated to annotated
John Benjamins ; https://doi.org/10.1075/ijcl.24137.rep
/content/journals/10.1075/ijcl.24137.rep
/content/journals/10.1075/ijcl.24137.rep
Loading

Data & Media loading...

  • Article Type: Research Article
Keywords: historical computational linguistics ; BERT embeddings ; eighteenth-century English ; register variation ; metadata

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error