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An Annotated Multilingual Dataset to Study Modality in the Gospels

Bibliographic Data

ID21835869
AuthorsHelena Bermúdez-Sabel, Helena Bermúdez Sabel (0000-0002-8627-1367), Francesca Dell'Oro
Year2024
Volume18
Issue1
Publication date2024-03-29
Open AccessYes
TypeARTICLE
VenueDigital humanities quarterly (JOURNAL)
Journal identifiersISSN: 1938-4122 • E-ISSN: 1938-4122
PublisherThe Association for Computers and the Humanities (PUBLISHER)
DOI10.63744/r386n4bcam37
OpenAlexW7139916092
LanguageEN

This paper presents a number of resources for examining the expression of modality in the Gospels. The main resource is an XML-TEI dataset that contains the linguistic annotation of a predefined list of potentially modal markers in both Ancient Greek and Latin. When one of these markers conveys a modal meaning, each constituent of the modal passage (i.e., the marker, its scope, and the modal relation between them) is annotated with a great level of detail through several linguistic features. One of the original features of our dataset is the implementation of a cross-referencing system that enables the alignment of the potentially modal markers of both languages. To facilitate the exploitation of our data by those unfamiliar with XML technologies, we also provide summary tables with the most relevant features of the annotation. In addition, a program written in Apache Ant allows any user to generate the summary sheets and to align modal passages in both Ancient Greek and Latin with any other language available in the Multilingual Bible Parallel Corpus . This contribution presents the details of the semantic annotation and its formalization, and how our resources may be exploited within semantics and translation studies. In addition, the encoding strategies implemented are relevant for other projects dealing with the combination of multiple layers of (linguistic) annotation and/or tackling the development of parallel corpora

Annotation · Modal · Semantic annotation · XML · Digital Humanities and Scholarship · Historical and Linguistic Studies · Natural Language Processing Techniques

Highly citedNo

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