Linguistic Tests for Discourse Relations in the TüBa-D/Z Corpus of Written German
Summary
Discourse relations, such as causal links between clauses, matter for text analysis beyond single sentences. They are notoriously hard to annotate consistently because there is little agreement on which phenomena to target or how they interact. Versley and Gastel argue that this is manageable if a scheme explicitly defines what kinds of entities a relation connects. That clarity limits annotation scope and lets annotators draw on linguistic theory while the scheme stays theory-neutral. The authors survey the main design decisions: which segment pairs to annotate, how to handle implicit relations and hierarchical structure, and how large and fine-grained the label set should be. They contrast RST, PDTB/D-LTAG, SDRT and related proposals. They then describe the scheme applied to selected newspaper articles in the TüBa-D/Z treebank of German. Linguistic tests, such as inserting connectives or implicit questions, help annotators choose between competing relations. Agreement is moderate between individual annotators and high after adjudication. This suggests that discourse annotation can be reproducible enough to serve as a shared resource.
Key points
- Resource: a discourse-relation layer added to articles from the TüBa-D/Z treebank. It sits alongside the existing syntactic and referential layers and has a scope similar to PDTB.
- Inter-annotator agreement: measured on seven documents. In the pilot study, agreement on single relations was κ = 0.55, rising to κ = 0.65 at the intermediate level of the taxonomy (nine types). The second study reached κ = 0.59 and κ = 0.69.
- Inter-adjudicator agreement: κ = 0.83/0.85 in the pilot and κ = 0.90/0.91 in the second study, where 167 spans (78%) matched. Disagreements dropped by roughly 57% to two thirds.
- Topic segmentation: κ = 0.71, with F1 = 0.62 on non-trivial boundaries. Transitional EDUs reached κ = 0.85 and F1 = 0.88.
- Main confusions: Restatement versus Explanation-cause/epistemic, and differing degrees of causality (temporal vs. enable vs. cause). These are linked to problems of event identity and parthood, which also affect schemes like TimeML.
Cited by
- Identifying Explicit Discourse Connectives in German
- Learning Connective-based Word Representations for Implicit Discourse Relation Identification
- Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection
- Categories of coherence relations in discourse annotation
Cite
@inproceedings{versley2013linguistic,
title = {{Linguistic Tests for Discourse Relations in the TüBa-D/Z Corpus of Written German}},
author = {Versley, Yannick and Gastel, Anna},
booktitle = {Dialogue \& Discourse Volume 4},
year = {2013},
doi = {10.5087/dad.2013.207},
url = {https://doi.org/10.5087/dad.2013.207}
}References
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