DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation

Christina Lohr, Udo Hahn


Abstract
We present DOPA METER, a tool suite for the metrical investigation of written language, that provides diagnostic means for its division into discourse categories, such as registers, genres, and style. The quantitative basis of our system are 120 metrics covering a wide range of lexical, syntactic, and semantic features relevant for language profiling. The scores can be summarized, compared, and aggregated using visualization tools that can be tailored according to the users’ needs. We also showcase an application scenario for DOPA METER.
Anthology ID:
2023.emnlp-demo.18
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2023
Address:
Singapore
Editors:
Yansong Feng, Els Lefever
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
218–228
Language:
URL:
https://aclanthology.org/2023.emnlp-demo.18
DOI:
10.18653/v1/2023.emnlp-demo.18
Bibkey:
Cite (ACL):
Christina Lohr and Udo Hahn. 2023. DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 218–228, Singapore. Association for Computational Linguistics.
Cite (Informal):
DOPA METER – A Tool Suite for Metrical Document Profiling and Aggregation (Lohr & Hahn, EMNLP 2023)
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 https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-demo.18.mp4