Named Entity Recognition and Resolution for Literary Studies (original) (raw)
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Namescape: Named Entity Recognition from a Literary Perspective
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The project Namescape: Mapping the Landscape of Names in Modern Dutch Literature (2012-2013) was a demonstrator project granted in the third CLARIN-NL call. Partners in the project were the Huygens Institute for the History of the Netherlands, the University of Amsterdam, and the Dutch Language Institute (CLARIN centre). The project dealt with Named Entity Recognition (NER) for modern Dutch ction and delivered two new NER tools for this purpose. It also addressed Named Entity Resolution and focused on a set of visualisations of names in individual texts from the corpus. This chapter gives an overview of the results of the project, starting with a description of the background of the research questions in the discipline of comparative literary onomastics. It then goes on to describe the tools that were delivered, and which can be found on the project website,
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This paper provides a description and evaluation of a generic named-entity recognition (NER) system for Swedish applied to electronic versions of Swedish literary classics from the 19th century. We discuss the challenges posed by these texts and the necessary adaptations introduced into the NER system in order to achieve accurate results, useful both for metadata generation, but also for the enhancement of the searching and browsing capabilities of Litteraturbanken, the Swedish Literature Bank, an ongoing cultural heritage project which aims to digitize significant works of Swedish literature.
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Protagonists' Tagger in Literary Domain - New Datasets and a Method for Person Entity Linkage
ArXiv, 2021
Semantic annotation of long texts, such as novels, remains an open challenge in Natural Language Processing (NLP). This research investigates the problem of detecting person entities and assigning them unique identities, i.e., recognizing people (especially main characters) in novels. We prepared a method for person entity linkage (named entity recognition and disambiguation) and new testing datasets. The datasets comprise 1,300 sentences from 13 classic novels of different genres that a novel reader had manually annotated. Our process of identifying literary characters in a text, implemented in protagonistTagger, comprises two stages: (1) named entity recognition (NER) of persons, (2) named entity disambiguation (NED) – matching each recognized person with the literary character’s full name, based on approximate text matching. The protagonistTagger achieves both precision and recall of above 83% on the prepared testing sets. Finally, we gathered a corpus of 13 full-text novels tagg...
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This paper reviews a stage of the process of annotating named entities in 18th-century texts to enrich historical research sources and link them to other bases. The categories in question are person, location and organisation, valid categories for historian analysis. We discuss the difficulties observed in the process and point eventual solutions.
LREC Marseille, 2020
We present NordiCon, a database containing medieval Nordic personal names attested in Continental sources. The database combines formally interpreted and richly interlinked onomastic data with digitized versions of the medieval manuscripts from which the data originate and information on the tokens' context. The structure of NordiCon is inspired by other online historical given name dictionaries. It takes up challenges reported on in previous works, such as how to cover material properties of a name token and how to define lemmatization principles, and elaborates on possible solutions. The lemmatization principles for NordiCon are further developed in order to facilitate the linking to other name dictionaries and corpora, and the integration of the database into Språkbanken Text, an infrastructure containing modern and historical written data.
Named-Entity Dataset for Medieval Latin, Middle High German and Old Norse
Journal of Open Humanities Data, 2021
We present a dataset of named entities in three languages: Medieval Latin, Middle High German and Old Norse. The dataset, containing proper nouns of persons and places, was originally created to extract characters from three related medieval texts. Since the annotation is on low-resource pre-modern languages, they may be important to build named-entity recognition tools for languages with little data and high linguistic variation.