% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
@misc{data_augmentation_2020,
      title={{An Analysis of Simple Data Augmentation for Named Entity Recognition}}, 
      author={Xiang Dai and Heike Adel},
      year={2020},
      eprint={2010.11683},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@inproceedings{social-groups-2023,
    title = "Detecting intersectionality in {NER} models: A data-driven approach",
    author = "Lassen, Ida Marie S.  and
      Almasi, Mina  and
      Enevoldsen, Kenneth  and
      Kristensen-McLachlan, Ross Deans",
    editor = "Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Reiter, Nils  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.latechclfl-1.13",
    doi = "10.18653/v1/2023.latechclfl-1.13",
    pages = "116--127",
    abstract = "The presence of bias is a pressing concern for both engineers and users of language technology. What is less clear is how exactly bias can be measured, so as to rank models relative to the biases they display. Using an innovative experimental method involving data augmentation, we measure the effect of intersectional biases in Danish models used for Name Entity Recognition (NER). We quantify differences in representational biases, understood as a systematic difference in error or what is called error disparity. Our analysis includes both gender and ethnicity to illustrate the effect of multiple dimensions of bias, as well as experiments which look to move beyond a narrowly binary analysis of gender. We show that all contemporary Danish NER models perform systematically worse on non-binary and minority ethnic names, while not showing significant differences for typically Danish names. Our data augmentation technique can be applied on other languages to test for biases which might be relevant for researchers applying NER models to the study of cultural heritage data.",
}

@mastersthesis{Katsarou1618310,
   author = {Katsarou, Styliani},
   institution = {KTH, School of Electrical Engineering and Computer Science (EECS)},
   pages = {80},
   school = {KTH, School of Electrical Engineering and Computer Science (EECS)},
   title = {{Improving Multilingual Models for the Swedish Language : Exploring CrossLingual Transferability and Stereotypical Biases}},
   series = {TRITA-EECS-EX},
   number = {2021:829},
   keywords = {Deep Learning, Natural Language Processing, Transformers, Multilingual Models, Cross-Lingual Transfer, Stereotypical Biases},
   abstract = {The best performing Transformer-based Language Models are monolingual and mainly focus on high-resource languages such as English. In an attempt to extend their usage to more languages, multilingual models have been introduced. Nevertheless, multilingual models still underperform on a specific language when compared to a similarly sized monolingual model that has been trained solely on that specific language. The main objective of this thesis project is to explore how a multilingual model can be improved for Swedish which is a low-resource language. We study if a multilingual model can benefit from further pre-training on Swedish or on a mix of English and Swedish text before fine-tuning. Our results on the task of semantic text similarity show that further pre-training increases the Pearson Correlation Score by 5% for specific cross-lingual language settings. Taking into account the responsibilities that arise from the increased use of Language Models in real-world applications, we supplement our work by additional experiments that measure stereotypical biases associated to gender. We use a new dataset that we designed specifically for that purpose. Our systematic study compares Swedish to English as well as various model sizes. The insights from our exploration indicate that the Swedish language carries less bias associated to gender than English and that higher manifestation of gender bias is associated to the use of larger Language Models.  },
   year = {2021}
}


@inbook{swe-diachronic,
url = {https://doi.org/10.1515/9783110767377-022},
title = {Swedish Diachronic Corpus},
booktitle = {CLARIN},
booktitle = {The Infrastructure for Language Resources},
author = {Eva Pettersson and Lars Borin},
editor = {Darja Fišer and Andreas Witt},
publisher = {De Gruyter},
address = {Berlin, Boston},
pages = {561--586},
doi = {doi:10.1515/9783110767377-022},
isbn = {9783110767377},
year = {2022},
lastchecked = {2023-12-17}
}


@inproceedings{papadopoulou-etal-2022-neural,
    title = "Neural Text Sanitization with Explicit Measures of Privacy Risk",
    author = "Papadopoulou, Anthi  and
      Yu, Yunhao  and
      Lison, Pierre  and
      {\O}vrelid, Lilja",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.aacl-main.18",
    pages = "217--229",
    abstract = "We present a novel approach for text sanitization, which is the task of editing a document to mask all (direct and indirect) personal identifiers and thereby conceal the identity of the individuals(s) mentioned in the text. In contrast to previous work, the approach relies on explicit measures of privacy risk, making it possible to explicitly control the trade-off between privacy protection and data utility. The approach proceeds in three steps. A neural, privacy-enhanced entity recognizer is first employed to detect and classify potential personal identifiers. We then determine which entities, or combination of entities, are likely to pose a re-identification risk through a range of privacy risk assessment measures. We present three such measures of privacy risk, respectively based on (1) span probabilities derived from a BERT language model, (2) web search queries and (3) a classifier trained on labelled data. Finally, a linear optimization solver decides which entities to mask to minimize the semantic loss while simultaneously ensuring that the estimated privacy risk remains under a given threshold. We evaluate the approach both in the absence and presence of manually annotated data. Our results highlight the potential of the approach, as well as issues specific types of personal data can introduce to the process.",
}


@inproceedings{multilingual-findings,
    title = "Finding Universal Grammatical Relations in Multilingual {BERT}",
    author = "Chi, Ethan A.  and
      Hewitt, John  and
      Manning, Christopher D.",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.acl-main.493",
    doi = "10.18653/v1/2020.acl-main.493",
    pages = "5564--5577",
    abstract = "Recent work has found evidence that Multilingual BERT (mBERT), a transformer-based multilingual masked language model, is capable of zero-shot cross-lingual transfer, suggesting that some aspects of its representations are shared cross-lingually. To better understand this overlap, we extend recent work on finding syntactic trees in neural networks{'} internal representations to the multilingual setting. We show that subspaces of mBERT representations recover syntactic tree distances in languages other than English, and that these subspaces are approximately shared across languages. Motivated by these results, we present an unsupervised analysis method that provides evidence mBERT learns representations of syntactic dependency labels, in the form of clusters which largely agree with the Universal Dependencies taxonomy. This evidence suggests that even without explicit supervision, multilingual masked language models learn certain linguistic universals.",
}


@inproceedings{conll2003,
    title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
    author = "Tjong Kim Sang, Erik F.  and
      De Meulder, Fien",
    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
    year = "2003",
    url = "https://aclanthology.org/W03-0419",
    pages = "142--147",
}


@inproceedings{semeval,
    title = "{{S}em{E}val-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts ({DDIE}xtraction 2013)}",
    author = "Segura-Bedmar, Isabel  and
      Mart{\'\i}nez, Paloma  and
      Herrero-Zazo, Mar{\'\i}a",
    editor = "Manandhar, Suresh  and
      Yuret, Deniz",
    booktitle = "Second Joint Conference on Lexical and Computational Semantics (*{SEM}), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation ({S}em{E}val 2013)",
    month = jun,
    year = "2013",
    address = "Atlanta, Georgia, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S13-2056",
    pages = "341--350",
}

@inproceedings{muc1993,
    title = {{MUC}-5 Evaluation Metrics},
    author = "Chinchor, Nancy  and
      Sundheim, Beth",
    booktitle = "Fifth Message Understanding Conference ({MUC}-5): Proceedings of a Conference Held in Baltimore, {M}aryland, August 25-27, 1993",
    year = "1993",
    url = "https://aclanthology.org/M93-1007",
}

@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}

@inProceedings{Dalin2011,
	title        = {{Semantic Search in Literature as an e-Humanities Research Tool: CONPLISIT – Consumption Patterns and Life-Style in 19th Century Swedish Literature}},
	abstract     = {We present our ongoing work on language technology-based e-science in the humanities, with a focus on text-based research in the historical sciences. Currently, we are
working on the adaptation and integration of lexical resources representing different historical stages of Swedish into a lexical
and morphological toolbox that will allow us to develop semantically oriented text search applications for historical research on Swedish text. We describe a semantic search prototype which was built using REST web services from this toolbox
as components, and which has been
evaluated by historians interested in using digitized 19th century novels as primary data for an historical investigation of the emerging consumer society in 19th century
Sweden.},
	booktitle    = {NEALT Proceedings Series (NODALIDA 2011 Conference Proceedings)},
	author       = {Borin, Lars and Forsberg, Markus and Ahlberger, Christer},
	year         = {2011},
	volume       = {11},
	pages        = {58--65},
}

@phdthesis{pettersson2016,
   author = {Pettersson, Eva},
   institution = {Uppsala University, Department of Linguistics and Philology},
   pages = {147},
   school = {Uppsala University, Department of Linguistics and Philology},
   title = {{Spelling Normalisation and Linguistic Analysis of Historical Text for Information Extraction}},
   series = {Studia Linguistica Upsaliensia},
   ISSN = {1652-1366},
   number = {17},
   keywords = {NLP for historical text, spelling normalisation, digital humanities, information extraction, character-based statistical machine translation, SMT, Levenshtein edit distance, language technology, computational linguistics},
   abstract = {Historical text constitutes a rich source of information for historians and other researchers in humanities. Many texts are however not available in an electronic format, and even if they are, there is a lack of NLP tools designed to handle historical text. In my thesis, I aim to provide a generic workflow for automatic linguistic analysis and information extraction from historical text, with spelling normalisation as a core component in the pipeline. In the spelling normalisation step, the historical input text is automatically normalised to a more modern spelling, enabling the use of existing taggers and parsers trained on modern language data in the succeeding linguistic analysis step. In the final information extraction step, certain linguistic structures are identified based on the annotation labels given by the NLP tools, and ranked in accordance with the specific information need expressed by the user. An important consideration in my implementation is that the pipeline should be applicable to different languages, time periods, genres, and information needs by simply substituting the language resources used in each module. Furthermore, the reuse of existing NLP tools developed for the modern language is crucial, considering the lack of linguistically annotated historical data combined with the high variability in historical text, making it hard to train NLP tools specifically aimed at analysing historical text. In my evaluation, I show that spelling normalisation can be a very useful technique for easy access to historical information content, even in cases where there is little (or no) annotated historical training data available. For the specific information extraction task of automatically identifying verb phrases describing work in Early Modern Swedish text, 91 out of the 100 top-ranked instances are true positives in the best setting.  },
   ISBN = {978-91-554-9443-8},
   year = {2016}
}


@misc{SUCcorpus,
  author = {Språkbanken},
  title = {{SUCX 3.0 - Stockholm-Umeå} corpus 3.0, scrambled},
  url = {https://spraakbanken.gu.se/en/resources/sucx3},
  year = {2023}
}

@misc{KBmodel,
  author = {Kurtz, Robin and Öhman, Joey},
  title = {{The KBLab Blog: SUCX 3.0 - NER}},
  url = {https://kb-labb.github.io/posts/2022-02-07-sucx3_ner/},
  year = {2022}
}

@phdthesis{reform,
   author = {Jansson, Martin},
   institution = {Uppsala University, Department of History of Science and Ideas},
   pages = {164},
   school = {Uppsala University, Department of History of Science and Ideas},
   title = {{Samtidens gr{\"a}nser : Om spr{\aa}kreformer och historisk tid runt sekelskiftet 1900}},
   series = {Uppsala Studies in History of Ideas},
   ISSN = {1653-5197},
   number = {59},
   keywords = {historical time, synchronisation, multiple temporalities, biblical criticism, phonetics, philology, translation, orthography},
   abstract = {This dissertation examines two extensive language reforms that caused controversy and discord in late 19th century Sweden: a comprehensive spelling reform and a formally authorised Bible translation. Among the numerous disagreements and entanglements that afflicted the projects, one issue persisted more than any other: the question of historical time. The correlation between past, present and future was fiercely contested and became a topic of intense negotiation. The purpose of this thesis is to analyse situated constructions of historical time around the turn of the 20th century. By invoking the language reforms as examples of temporal synchronisations, the study contributes to recent debates about modern temporalities and historical time. While earlier research predominantly considers intellectual discourses and abstract notions of temporality, this study emphasises historical time as a consequence of practical measures and applied procedures. The study concludes that historical time was established in order to manage a plethora of conflicting temporalities and asynchronous rhythms. The spelling reform and the Bible translation were affected by a wide range of conflicting times that preceded the notions of progress and acceleration often associated with the modern regime of historicity. The thesis demonstrates how the language reforms established a modern historical order that is often reduced to a passive articulation of an underlying regime. },
   ISBN = {978-91-513-1738-0},
   year = {2023}
}


@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {{The Theory of Parsing, Translation and Compiling}},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {{Alternation}},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={{Scalable training of {$L_1$}-regularized log-linear models}},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
  url={https://dl.acm.org/doi/abs/10.1145/1273496.1273501}
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {{Algorithms on Strings, Trees and Sequences}},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK},
    url={https://www.cambridge.org/core/books/algorithms-on-strings-trees-and-sequences/F0B095049C7E6EF5356F0A26686C20D3}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {{Yara Parser: {A} Fast and Accurate Dependency Parser}},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {{A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data}},
	Volume = {6},
	Year = {2005},
	url={https://www.jmlr.org/papers/volume6/ando05a/ando05a.pdf}
}

@article{ct1965,
  title={{An algorithm for the machine calculation of complex {F}ourier series}},
  author={Cooley, James W. and Tukey, John W.},
  journal={Mathematics of Computation},
  volume={19},
  number={90},
  pages={297--301},
  year={1965},
  url={https://www.ams.org/journals/mcom/1965-19-090/S0025-5718-1965-0178586-1/S0025-5718-1965-0178586-1.pdf}
}

@article{nadeau:2007,
author = {Nadeau, David and Sekine, Satoshi},
year = {2007},
month = {08},
pages = {},
title = {{A Survey of Named Entity Recognition and Classification}},
volume = {30},
journal = {Lingvisticae Investigationes},
doi = {10.1075/li.30.1.03nad}
}

@misc{Bridal1566701,
   author = {Bridal, Olle},
   institution = {Linköping University, Department of Computer and Information Science},
   pages = {22},
   school = {Linköping University, Department of Computer and Information Science},
   title = {{Named-entity recognition with BERT for anonymization of medical records}},
   keywords = {NLP, anonymization, de-identification, NER, BERT, medical records, Swedish},
   year = {2021}
}

@article{CanBertDigIt,
author = {Brandsen, Alex and Verberne, Suzan and Lambers, Karsten and Wansleeben, Milco},
title = {Can BERT Dig It? Named Entity Recognition for Information Retrieval in the Archaeology Domain},
year = {2022},
issue_date = {September 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {15},
number = {3},
issn = {1556-4673},
url = {https://doi.org/10.1145/3497842},
doi = {10.1145/3497842},
journal = {J. Comput. Cult. Herit.},
month = {sep},
articleno = {51},
numpages = {18},
keywords = {language modelling, BERT, Archaeology}
}

@misc{afo:20b,
    title = {{A new gold standard for Swedish named entity recognition: {Version} 1 contents}},
    author = "Ahrenberg, Lars and Frid, Johan and Olsson, Leif-Jöran",
    year = "2020",
    howpublished = "SWE-CLARIN Report Series SCR-01-2020",
    url = "https://sweclarin.se/swe/v%C3%A5ra-resurser/rapportserie"
}

@misc{NER-guidelines:24,
    title = "Named entity recognition in
19th century Swedish texts: Annotation guidelines",
    author = "Pettersson, Eva and Lenas, Erik and Borin, Lars and Dahlgren, Catharina",
    year = "forthcoming",
    howpublished = "SWE-CLARIN Report Series SCR-01-2024"
}
