Evaluating Syntactic Annotation of Ancient Languages

Author:

Biagetti Erica1,Hellwig Oliver2,Scarlata Salvatore3,Ackermann Elia4,Widmer Paul5

Affiliation:

1. University of Pavia / University of Bergamo, Department of Linguistics, Pavia, Italy, erica.biagetti01@universitadipavia.it

2. University of Zurich, Department of Comparative Linguistics, Center for the Interdisciplinary Study of Language Evolution / Heinrich Heine University Düsseldorf, Institute for Language and Information, Zürich, Switzerland, oliver.hellwig@uzh.ch

3. University of Zurich, Department of Comparative Linguistics, Center for the Interdisciplinary Study of Language Evolution, Zürich, Switzerland, salvatore.scarlata@uzh.ch

4. University of Zurich, Deutsches Seminar, Zürich, Switzerland, elia.ackermann@uzh.ch

5. University of Zurich, Department of Comparative Linguistics, Center for the Interdisciplinary Study of Language Evolution, Zürich, Switzerland, paul.widmer@uzh.ch

Abstract

Abstract In this paper we introduce an extended version of the Vedic Treebank (vtb, Hellwig et al. 2020) which comes along with revisited and extended annotation guidelines. In order to assess the quality of our annotations as well as the usability and limits of the guidelines we performed an inter-annotator agreement test. The results show that agreement between annotators is hampered by various factors, most prominently by insufficient understanding of the content because of the cultural and temporal gap and incomplete knowledge of Vedic grammar. An in-depth discussion of disagreeing annotations demonstrates that the setup of the workflow, too, has a major influence on inter-annotator agreement. We suggest some measures that can help increase the transparency and annotation consistency according to current knowledge of the language when annotating Vedic Sanskrit, or ancient language varieties in general.

Publisher

Brill

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Linguistic annotation of cuneiform texts using treebanks and deep learning;Digital Scholarship in the Humanities;2024-02-01

2. Data-driven dependency parsing of Vedic Sanskrit;Language Resources and Evaluation;2023-02-10

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