Annotation Challenges for Reconstructing the Structural Elaboration
            of Middle Low German

Seemann, Nina; Merten, Marie-Luis; Geierhos, Michaela; Tophinke, Doris; Hüllermeier, Eyke

doi:10.18653/v1/w17-2206

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The corpus considered in this study mainly stems from the InterGramm project 1 [15,22,23] and consists of dateable and localizable legal documents and records of judgements ranging from the 13th to the 17th century comprising 23 documents in total. The majority of the documents originate from the Central Low German region written in a historic German dialect called Middle Low German.…”

Section: Corpus Descriptionmentioning

confidence: 99%

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction

Heid,

Wever,

Hüllermeier

2020

Preprint

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Syntactic annotation of corpora in the form of part-of-speech ( ) tags is a key requirement for both linguistic research and subsequent automated natural language processing ( ) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a tagger on a sufficiently large corpus of labeled data. While the problem of tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography. These irregularities render the task of automated tagging more difficult and error-prone. Under these circumstances, instead of forcing the tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider tagging within the framework of set-valued prediction, which allows the tagger to express its uncertainty via predicting a set of candidate tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct tag is included while keeping the number of candidates small. In our experimental study, we find that extending state-of-the-art taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.

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Section: Corpus Descriptionmentioning

confidence: 99%

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction

Heid,

Wever,

Hüllermeier

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Annotation uncertainty in the context of grammatical change

Merten

Wever

Geierhos

et al. 2023

IJCL

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This paper elaborates on the notion of uncertainty in the context of annotation in large text corpora, specifically focusing on (but not limited to) historical languages. Such uncertainty might be due to inherent properties of the language, for example, linguistic ambiguity and overlapping categories of linguistic description, but could also be caused by a lack of annotation expertise. By examining annotation uncertainty in more detail, we identify the sources, deepen our understanding of the nature and different types of uncertainty encountered in daily annotation practice, and discuss practical implications of our theoretical findings. This paper can be seen as an attempt to reconcile the perspectives of the main scientific disciplines involved in corpus projects, linguistics and computer science, to develop a unified view and to highlight the potential synergies between these disciplines.

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Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German

Cited by 2 publications

References 10 publications

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction

Annotation uncertainty in the context of grammatical change

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