The MULI Project: Annotation and Analysis of Information Structure in German and English

What it is about

MULI is a pilot project that annotates information structure in German and English newspaper text without using theory-bound labels such as topic/focus or theme/rheme. The annotation works on three levels: syntax (non-canonical word order such as fronting, clefts, passives, there-insertion), discourse (each referent's type, delimitation, quantification and Prince's information status: new, unused, inferable, evoked; plus coreference and bridging links) and prosody (ToBI accents and breaks, read aloud by one German and one English speaker). The corpus is 250 German sentences (about 3,500 tokens) from the TIGER treebank and 320 English sentences (about 7,000 tokens) from the Penn Treebank, both economic news. Two annotators worked with MMAX. The paper has no accuracy figures. It shows one annotated English sequence in detail, where three of four clauses have non-canonical order, and it argues that the co-occurrence of the three levels is the way to see how each language marks information structure.

Ideas

• I1. An information-status labeller for English referents, using Prince's four classes (new, unused, inferable, evoked) and the rule that a definite NP whose head is linked to an earlier entity through a relation (the crash of the market) is inferable. Keep the link and the rule in the output.
• I2. Fronting detector over UD trees: a clause whose first constituent is an adjunct or object before the subject, with the subject after the finite verb, is marked as non-canonical. The paper's three fronted clauses in its example are a test set.
• I3. Co-occurrence table: how often fronting, passive and there-insertion occur with each information-status class. Build it from the parsed English texts in Mova's own data and report the counts, not a claim about the language.
• I4. Negative control: a sentence with canonical order and a new-information subject must not get the fronting label or the evoked-status label. Count false labels on a set of such sentences.

For Mova

The chapters are 29. Ellipsis, word order and information structure (fronting, clefts, there-insertion and information structure), 28. Voice and the passive (the passive that brings the patient to subject) and 35. Reference and coreference (bridging and the status of referents). The code is en/src/lin.rs (word order from the tree), coref/src/mention.rs (the mention and its status) and world/src/story.rs (the entities already in the text). Prosody is out of scope for Mova, which works on text only. The measures: precision of the fronting detector and of the information-status labeller on the paper's example sequence, run twice for identical output, and the false-label rate of the negative-control sentences.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.63317/5m6hv2qjdkze

@inproceedings{baumann2004muli,
  title = {{The MULI Project: Annotation and Analysis of Information Structure in German and English}},
  author = {Baumann, Stefan and Brinckmann, Caren and Hansen-Schirra, Silvia and Kruijff, Geert-Jan and Kruijff-Korbayová, Ivana and Neumann, Stella and Steiner, Erich and Teich, Elke and Uszkoreit, Hans},
  booktitle = {Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC'04)},
  year = {2004},
  doi = {10.63317/5m6hv2qjdkze},
  url = {https://doi.org/10.63317/5m6hv2qjdkze}
}

References

16 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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