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CoNLL-Merge: Efficient Harmonization of Concurrent Tokenization and Textual Variation

Authors Christian Chiarcos , Niko Schenk

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Christian Chiarcos
  • Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany
Niko Schenk
  • Applied Computational Linguistics Lab, Goethe University Frankfurt, Germany

Funding

The research described in this paper was supported by the project Linked Open Dictionaries (LiODi, 2015-2020), funded by the German Ministry for Education and Research (BMBF) and the project Machine Translation and Automated Analysis of Cuneiform Languages which is generously funded by the German Research Foundation (DFG), the Canadian Social Sciences and Humanities Research Council, and the National Endowment for the Humanities through the T-AP Digging into Data Challenge.

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Christian Chiarcos and Niko Schenk. CoNLL-Merge: Efficient Harmonization of Concurrent Tokenization and Textual Variation. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 7:1-7:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.LDK.2019.7

Abstract

The proper detection of tokens in of running text represents the initial processing step in modular NLP pipelines. But strategies for defining these minimal units can differ, and conflicting analyses of the same text seriously limit the integration of subsequent linguistic annotations into a shared representation. As a solution, we introduce CoNLL Merge, a practical tool for harmonizing TSV-related data models, as they occur, e.g., in multi-layer corpora with non-sequential, concurrent tokenizations, but also in ensemble combinations in Natural Language Processing. CoNLL Merge works unsupervised, requires no manual intervention or external data sources, and comes with a flexible API for fully automated merging routines, validity and sanity checks. Users can chose from several merging strategies, and either preserve a reference tokenization (with possible losses of annotation granularity), create a common tokenization layer consisting of minimal shared subtokens (loss-less in terms of annotation granularity, destructive against a reference tokenization), or present tokenization clashes (loss-less and non-destructive, but introducing empty tokens as place-holders for unaligned elements). We demonstrate the applicability of the tool on two use cases from natural language processing and computational philology.

Subject Classification

ACM Subject Classification
  • Applied computing → Format and notation
  • Applied computing → Document management and text processing
  • Applied computing → Annotation
  • Software and its engineering → Interoperability
Keywords
  • data heterogeneity
  • tokenization
  • tab-separated values (TSV) format
  • linguistic annotation
  • merging

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