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Models for MapReader ACM SIGSPATIAL 2023 Geohumanities Workshop paper
(2023)
Hosseini, Kasra
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Beelen, Kaspar
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McDonough, Katherine
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Wilson, Daniel C. S.
Collection of fine-tuned models created during research published in Kasra Hosseini, Daniel C. S. Wilson, Kaspar Beelen, and Katherine McDonough. 2022. MapReader: a computer vision pipeline for the semantic exploration of maps at scale. In Proceedings of the 6th ACM SIGSPATIAL International Workshop on Geospatial Humanities (GeoHumanities '22). Association for Computing Machinery, New York, NY, USA, 8–19. https://doi.org/10.1145/3557919.3565812
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A Dataset for Toponym Resolution in Nineteenth-Century English Newspapers
(2022-01-24)
Coll Ardanuy, Mariona
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Beavan, David
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Beelen, Kaspar
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Hosseini, Kasra
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Lawrence, Jon
We present a new dataset for the task of toponym resolution in digitized historical newspapers in English. It consists of 343 annotated articles from newspapers based in four different locations in England (Manchester, Ashton-under-Lyne, Poole and Dorchester), published between 1780 and 1870. The articles have been manually annotated with mentions of places, which are linked—whenever possible—to their corresponding entry on Wikipedia. The dataset consists of 3,364 annotated toponyms, of which 2,784 have been provided with a link to Wikipedia. The dataset is published in the British Library shared research repository, and is especially of interest to researchers working on improving semantic access to historical newspaper content.
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Maps of a Nation? The Digitized Ordnance Survey for New Historical Research
(2021)
Hosseini, Kasra
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McDonough, Katherine
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van Strien, Daniel
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Vane, Olivia
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Wilson, Daniel C.S.
Although the Ordnance Survey has itself been the subject of historical research, scholars have not systematically used its maps as primary sources of information. This is partly for disciplinary reasons and partly for the technical reason that high-quality maps have not until recently been available digitally, geo-referenced, and in color. A final, and crucial, addition has been the creation of item-level metadata which allows map collections to become corpora which can for the first time be interrogated en masse as source material. By applying new Computer Vision methods leveraging machine learning, we outline a research pipeline for working with thousands (rather than a handful) of maps at once, which enables new forms of historical inquiry based on spatial analysis. Our ‘patchwork method’ draws on the longstanding desire to adopt an overall or ‘complete’ view of a territory, and in so doing highlights certain parallels between the situation faced by today’s users of digitized maps, and a similar inflexion point faced by their predecessors in the nineteenth century, as the project to map the nation approached a form of completion.
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MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale
(2021)
Hosseini, Kasra
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Wilson, Daniel C.S.
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Beelen, Kaspar
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McDonough, Katherine
We present MapReader, a free, open-source software library written in Python for analyzing large map collections (scanned or born-digital). This library transforms the way historians can use maps by turning extensive, homogeneous map sets into searchable primary sources. MapReader allows users with little or no computer vision expertise to i) retrieve maps via web-servers; ii) preprocess and divide them into patches; iii) annotate patches; iv) train, fine-tune, and evaluate deep neural network models; and v) create structured data about map content. We demonstrate how MapReader enables historians to interpret a collection of â‰ˆ16K nineteenth-century Ordnance Survey map sheets (â‰ˆ30.5M patches), foregrounding the challenge of translating visual markers into machine-readable data. We present a case study focusing on British rail infrastructure and buildings as depicted on these maps. We also show how the outputs from the MapReader pipeline can be linked to other, external datasets, which we use to evaluate as well as enrich and interpret the results. We release â‰ˆ62K manually annotated patches used here for training and evaluating the models.
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Neural Language Models for Nineteenth-Century English
(2021)
Hosseini, Kasra
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Beelen, Kaspar
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Colavizza, Giovanni
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Coll Ardanuy, Mariona
We present four types of neural language models trained on a large historical dataset of books in English, published between 1760-1900 and comprised of ~5.1 billion tokens. The language model architectures include static (word2vec and fastText) and contextualized models (BERT and Flair). For each architecture, we trained a model instance using the whole dataset. Additionally, we trained separate instances on text published before 1850 for the two static models, and four instances considering different time slices for BERT. Our models have already been used in various downstream tasks where they consistently improved performance. In this paper, we describe how the models have been created and outline their reuse potential.
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Contextualizing Victorian Newspapers
(2020)
Beelen, Kaspar
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Ahnert, Ruth
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Beavan, David
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Coll Ardanuy, Mariona
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Hosseini, Kasra
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A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
(2020)
Coll Ardanuy, Mariona
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Hosseini, Kasra
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McDonough, Katherine
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Krause, Amrey
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van Strien, Daniel
Recognizing toponyms and resolving them to their real-world referents is required for providing advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a toponym previously recognized. While it has traditionally received little attention in the research community, it has been shown that candidate selection has a significant impact on downstream tasks (i.e. entity resolution), especially in noisy or non-standard text. In this paper, we introduce a flexible deep learning method for candidate selection through toponym matching, using state-of-the-art neural network architectures. We perform an intrinsic toponym matching evaluation based on several new realistic datasets, which cover various challenging scenarios (cross-lingual and regional variations, as well as OCR errors). We report its performance on candidate selection in the context of the downstream task of toponym resolution, both on existing datasets and on a new manually-annotated resource of nineteenth-century English OCR'd text.
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Living Machines: A study of atypical animacy
(2020)
Coll Ardanuy, Mariona
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Nanni, Federico
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Beelen, Kaspar
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Hosseini, Kasra
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Ahnert, Ruth
This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate objects, specifically machines, are given animate attributes. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate. Our method builds on recent innovations in language modeling, specifically BERT contextualized word embeddings, to better capture fine-grained contextual properties of words. We present a fully unsupervised pipeline, which can be easily adapted to different contexts, and report its performance on an established animacy dataset and our newly introduced resource. We show that our method provides a substantially more accurate characterization of atypical animacy, especially when applied to highly complex forms of language use.
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When Time Makes Sense: A Historically-Aware Approach to Targeted Sense Disambiguation
(2021)
Beelen, Kaspar
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Nanni, Federico
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Coll Ardanuy, Mariona
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Hosseini, Kasra
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Tolfo, Giorgia
As languages evolve historically, making computational approaches sensitive to time can improve performance on specific tasks. In this work, we assess whether applying historical language models and time-aware methods help with determining the correct sense of polysemous words. We outline the task of time-sensitive Targeted Sense Disambiguation (TSD), which aims to detect instances of a sense or set of related senses in historical and time-stamped texts, and address two main goals: 1) we scrutinize the effect of applying historical language models on the performance of several TSD methods and 2) we assess different disambiguation methods that take into account the year in which a text was produced. We train historical BERT models on a corpus of nineteenth-century English books and draw on the Oxford English Dictionary (and its Historical Thesaurus) to create historically evolving sense representations. Our results show that using historical language models consistently improves performance whereas timesensitive disambiguation helps especially with older documents.
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DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching
(2020-10)
Hosseini, Kasra
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Nanni, Federico
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Coll Ardanuy, Mariona
We present DeezyMatch, a free, open-source software library written in Python for fuzzy string matching and candidate ranking. Its pair classifier supports various deep neural network architectures for training new classifiers and for fine-tuning a pretrained model, which paves the way for transfer learning in fuzzy string matching. This approach is especially useful where only limited training examples are available. The learned DeezyMatch models can be used to generate rich vector representations from string inputs. The candidate ranker component in DeezyMatch uses these vector representations to find, for a given query, the best matching candidates in a knowledge base. It uses an adaptive searching algorithm applicable to large knowledge bases and query sets. We describe DeezyMatch’s functionality, design and implementation, accompanied by a use case in toponym matching and candidate ranking in realistic noisy datasets.
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