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It does this through a case study on idiom token classification. We use idiom token identification as a basis for our analysis because of the variety of information types that have previously been explored in the literature for this task, including: topic, lexical and syntactic features. This variety of relevant information types means that the task of idiom token identification enables us to explore the forms of linguistic information that a BERT language model captures and encodes in their representations. The core of the paper presents three experiments. The first experiment analyzes the effectiveness of BERT sentence embeddings for creating a general idiom token identification model and the results indicate that the BERT sentence embeddings outperform Skip-Thought. In the second and third experiment we use the game theory concept of Shapley Values to rank the usefulness of individual idiomatic expressions for model training and use this ranking to analyse the type of information that the model finds useful. We find that a combination of idiom-intrinsic and topic-based properties contribute to an expression’s usefulness in idiom token identification. Overall our results indicate that BERT efficiently encodes a variety of information from topic, through lexical and syntactic information. 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Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).", "proceedingsTitle": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", "conferenceName": "NAACL-HLT 2019", "publisher": "Association for Computational Linguistics", "place": "Minneapolis, Minnesota", "date": "2019-06", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "4171–4186", "series": "", "seriesNumber": "", "DOI": "10.18653/v1/N19-1423", "ISBN": "", "citationKey": "", "url": "https://aclanthology.org/N19-1423/", "accessDate": "2026-04-12T10:16:36Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "BERT", "language": "", "libraryCatalog": "ACLWeb", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "BK5N9ESA" ], "relations": {}, "dateAdded": "2026-04-12T10:16:36Z", "dateModified": "2026-04-12T10:16:36Z" } }, { "key": "63ZUVRHS", "version": 1211, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/63ZUVRHS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/63ZUVRHS", "type": "text/html" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } }, "creatorSummary": "Davis et al.", "parsedDate": "2021", "numChildren": 0 }, "data": { "key": "63ZUVRHS", "version": 1211, "itemType": "journalArticle", "title": "Can I believe what I see? Data visualization and trust in the humanities", "creators": [ { "creatorType": "author", "firstName": "Stephen Boyd", "lastName": "Davis" }, { "creatorType": "author", "firstName": "Olivia", "lastName": "Vane" }, { "creatorType": "author", "firstName": "Florian", "lastName": "Kräutli" } ], "abstractNote": "Questions of trust are increasingly important in relation to data and its use. The authors focus on humanities data and its visualization, through analysis of their own recent projects with museums, archives and libraries internationally. Their account connects the specifics of hands-on digital humanities work to larger epistemological questions. They discuss the sources of potential mistrust, and examine how different expectations and assumptions emerge depending on the use and user of the data; they offer a simple schema through which the implications may be traced. It is argued that vital issues of trust can be engaged with through design, which, rather than being conceived as a cosmetic finish, is seen as contributing insights and questions that affect the whole process. The article concludes with recommendations intended to be useful in both theory and practice.", "publicationTitle": "Interdisciplinary Science Reviews", "publisher": "", "place": "", "date": "2021", "volume": "46", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "522-546", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1080/03080188.2021.1872874", "citationKey": "", "url": "https://journals.sagepub.com/doi/abs/10.1080/03080188.2021.1872874", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "_eprint: https://journals.sagepub.com/doi/pdf/10.1080/03080188.2021.1872874", "tags": [], "collections": [ "BK5N9ESA", "IJM2JQ2Z", "Z8C8A7FE" ], "relations": {}, "dateAdded": "2026-04-12T10:08:07Z", "dateModified": "2026-04-12T10:08:07Z" } }, { "key": "FEM9DRSC", "version": 1208, "library": { 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"abstractNote": "The selection and availability bias concerning digital archives can be a problem for empirical studies. In the context of historical correspondence networks, we still have limited information about how missing data can affect and hinder the variables of our interest. Along these lines, we introduce an agent-based model to reconstruct historical communication using letters. We use this model to simulate the letter-sending process and its subsequent archival. We use it to build counterfactual networks and learn how the missing data caused by the archival of the letters might bias the network statistics.", "publicationTitle": "Digital Scholarship in the Humanities", "publisher": "", "place": "", "date": "2025-12-01", "volume": "40", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "1101-1114", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Digital Scholarship Humanities", "DOI": "10.1093/llc/fqaf081", "citationKey": "", "url": "https://doi.org/10.1093/llc/fqaf081", "accessDate": "2026-03-15T11:56:10Z", "PMID": "", "PMCID": "", "ISSN": "2055-7671", "archive": "", "archiveLocation": "", "shortTitle": "Growing and pruning the archive", "language": "", "libraryCatalog": "Silverchair", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "ZXXH4BU3" ], "relations": {}, "dateAdded": "2026-03-15T11:56:10Z", "dateModified": "2026-03-15T11:56:10Z" } }, { "key": "Z6ML4J6W", "version": 1179, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/Z6ML4J6W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/Z6ML4J6W", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/207841/items/W5C7TJ82", "type": "application/json" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "Z6ML4J6W", "version": 1179, "parentItem": "W5C7TJ82", "itemType": "attachment", "linkMode": "imported_url", "title": "Preprint PDF", "accessDate": "2025-12-11T08:46:16Z", "url": "http://arxiv.org/pdf/2504.00414v1", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Greif et al. - 2025 - Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents.pdf", "md5": "e72ee239bf59c9f0d893ea5c4930dc17", "mtime": 1765442776416, "tags": [], "relations": {}, "dateAdded": "2025-12-11T08:46:16Z", "dateModified": "2025-12-11T08:46:16Z" } }, { "key": "XNYX7HIK", "version": 1180, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/XNYX7HIK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/XNYX7HIK", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/207841/items/W5C7TJ82", "type": "application/json" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } } }, "data": { "key": "XNYX7HIK", "version": 1180, "parentItem": "W5C7TJ82", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2025-12-11T08:45:57Z", "url": "http://arxiv.org/abs/2504.00414", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "2504.html", "md5": "d0d6a32941bbc1638a84ecb2c19ef860", "mtime": 1765442757893, "tags": [], "relations": {}, "dateAdded": "2025-12-11T08:45:57Z", "dateModified": "2025-12-11T08:45:57Z" } }, { "key": "W5C7TJ82", "version": 1177, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/W5C7TJ82", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/W5C7TJ82", "type": "text/html" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } }, "creatorSummary": "Greif et al.", "parsedDate": "2025-04-01", "numChildren": 2 }, "data": { "key": "W5C7TJ82", "version": 1177, "itemType": "preprint", "title": "Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents", "creators": [ { "creatorType": "author", "firstName": "Gavin", "lastName": "Greif" }, { "creatorType": "author", "firstName": "Niclas", "lastName": "Griesshaber" }, { "creatorType": "author", "firstName": "Robin", "lastName": "Greif" } ], "abstractNote": "We explore how multimodal Large Language Models (mLLMs) can help researchers transcribe historical documents, extract relevant historical information, and construct datasets from historical sources. Specifically, we investigate the capabilities of mLLMs in performing (1) Optical Character Recognition (OCR), (2) OCR Post-Correction, and (3) Named Entity Recognition (NER) tasks on a set of city directories published in German between 1754 and 1870. First, we benchmark the off-the-shelf transcription accuracy of both mLLMs and conventional OCR models. We find that the best-performing mLLM model significantly outperforms conventional state-of-the-art OCR models and other frontier mLLMs. Second, we are the first to introduce multimodal post-correction of OCR output using mLLMs. We find that this novel approach leads to a drastic improvement in transcription accuracy and consistently produces highly accurate transcriptions (<1% CER), without any image pre-processing or model fine-tuning. Third, we demonstrate that mLLMs can efficiently recognize entities in transcriptions of historical documents and parse them into structured dataset formats. Our findings provide early evidence for the long-term potential of mLLMs to introduce a paradigm shift in the approaches to historical data collection and document transcription.", "genre": "", "repository": "arXiv", "archiveID": "arXiv:2504.00414", "place": "", "date": "2025-04-01", "series": "", "seriesNumber": "", "DOI": "10.48550/arXiv.2504.00414", "citationKey": "", "url": "http://arxiv.org/abs/2504.00414", "accessDate": "2025-12-11T08:45:57Z", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "arXiv.org", "callNumber": "", "rights": "", "extra": "arXiv:2504.00414 [cs]", "tags": [ { "tag": "Computer Science - Artificial Intelligence", "type": 1 }, { "tag": "Computer Science - Computation and Language", "type": 1 }, { "tag": "Computer Science - Digital Libraries", "type": 1 } ], "collections": [ "ZXXH4BU3" ], "relations": {}, "dateAdded": "2025-12-11T08:45:57Z", "dateModified": "2025-12-11T08:45:57Z" } }, { "key": "VNGGTA4U", "version": 1175, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/VNGGTA4U", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/VNGGTA4U", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/207841/items/2QNB4QHH", "type": "application/json" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "VNGGTA4U", "version": 1175, "parentItem": "2QNB4QHH", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2025-12-11T08:42:34Z", "url": "https://jdmdh.episciences.org/11090/pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Guéville and Wrisley - 2024 - Transcribing Medieval Manuscripts for Machine Learning.pdf", "md5": "f39840585cd955a8bcb3d087005ba9c7", "mtime": 1765442554160, "tags": [], "relations": {}, "dateAdded": "2025-12-11T08:42:34Z", "dateModified": "2025-12-11T08:42:34Z" } }, { "key": "2QNB4QHH", "version": 1188, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/2QNB4QHH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/2QNB4QHH", "type": "text/html" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } }, "lastModifiedByUser": { "id": 285767, "username": "joris.van.zundert", "name": "Joris", "links": { "alternate": { "href": "https://www.zotero.org/joris.van.zundert", "type": "text/html" } } }, "creatorSummary": "Guéville and Wrisley", "parsedDate": "2024-07-02", "numChildren": 1 }, "data": { "key": "2QNB4QHH", "version": 1188, "itemType": "journalArticle", "title": "Transcribing Medieval Manuscripts for Machine Learning", "creators": [ { "creatorType": "author", "firstName": "Estelle", "lastName": "Guéville" }, { "creatorType": "author", "firstName": "David Joseph", "lastName": "Wrisley" } ], "abstractNote": "This article focuses on the transcription of medieval manuscripts. Whereas problems of transcription have long interested medievalists, few workable options in the era of printed editions were available besides normalisation. The automation of this process, known as handwritten text recognition (HTR), has made new kinds of digital text creation possible, but also has foregrounded the necessity of theorising transcription in our scholarly practices. We reflect here on different notions of transcription against the backdrop of changing text technologies. Moreover, drawing on our own research on medieval Latin Bibles, we present general guidelines for customizing transcription schemes, arguing that they must be designed with specific research questions and scholarly end use in mind. Since we are particularly interested in the scribal contribution to the production of codices, our transcription guidelines aim to capture abbreviations and orthographic variation between different textual witnesses for downstream machine learning tasks. In the final section of the article, we discuss a few examples of how the HTR-created transcriptions allow us to address new questions at scale in medieval manuscripts, such as textual variance across witnesses, the prediction of a change in scribal hands within a single manuscript as well as the profiling of individual and regional scribal characteristics.", "publicationTitle": "Journal of Data Mining & Digital Humanities", "publisher": "Episciences.org", "place": "", "date": "2024-07-02", "volume": "On the Way to the Future of Digital Manuscript Studies", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.46298/jdmdh.9805", "citationKey": "", "url": "https://jdmdh.episciences.org/11090", "accessDate": "2025-12-11T08:42:33Z", "PMID": "", "PMCID": "", "ISSN": "2416-5999", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": 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"2025-11-06T13:00:56Z" } }, { "key": "6MAIDVYG", "version": 1188, "library": { "type": "group", "id": 207841, "name": "Doing DH / Intro to DH", "links": { "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/207841/items/6MAIDVYG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/doing_dh__intro_to_dh/items/6MAIDVYG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 990935, "username": "tla", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tla", "type": "text/html" } } }, "lastModifiedByUser": { "id": 285767, "username": "joris.van.zundert", "name": "Joris", "links": { "alternate": { "href": "https://www.zotero.org/joris.van.zundert", "type": "text/html" } } }, "creatorSummary": "Pleska et al.", "parsedDate": "2019-10", "numChildren": 1 }, "data": { "key": "6MAIDVYG", "version": 1188, "itemType": "journalArticle", "title": "A Framework for Interrogating Social Media Images to Reveal an Emergent Archive of War", "creators": [ { "creatorType": "author", "firstName": "Arijus", "lastName": "Pleska" }, { "creatorType": "author", "firstName": "Andrew", "lastName": "Hoskins" }, { "creatorType": "author", "firstName": "Karen", "lastName": "Renaud" } ], "abstractNote": "The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.\nHowever, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).\nTo this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. 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