Models for "A data-driven approach to studying changing vocabularies in historical newspaper collections"

Hengchen, Simon; Ros, Ruben; Marjanen, Jani; Tolonen, Mikko

doi:10.5281/zenodo.3585027

Cited by 2 publications

(1 citation statement)

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“…They were computed from many different corpora by using word2vec skip-gram with negative sampling. Later on, Hengchen et al [83] released a set of diachronic embeddings of the same type in English, Dutch, Finnish and Swedish trained on large corpora of 19C-20C newspapers. 30 More recently, Hengchen et al [84] pursued these efforts with the publication of diachronic word2vec and fastText models trained on a large corpus of Swedish OCRed newspapers (1645-1926) (the Kubhist 2 corpus, 5.5 billion tokens).…”

Section: Static Embeddingsmentioning

confidence: 99%

Named Entity Recognition and Classification on Historical Documents: A Survey

Ehrmann¹,

Hamdi²,

Pontes³

et al. 2021

Preprint

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After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and accessibility, it also opens up new opportunities in terms of content mining and the next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve and explore information from this 'big data of the past'. Among semantic indexing opportunities, the recognition and classification of named entities are in great demand among humanities scholars. Yet, named entity recognition (NER) systems are heavily challenged with diverse, historical and noisy inputs. In this survey, we present the array of challenges posed by historical documents to NER, inventory existing resources, describe the main approaches deployed so far, and identify key priorities for future developments.

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Section: Static Embeddingsmentioning

confidence: 99%