The Merged Upper Model: A Linguistic Ontology for German and English

What it is about

The paper merges two linguistically motivated upper models used for sentence generation: the English Upper Model (about 252 concepts after removing rhetorical relations) and the German Upper Model (about 235 concepts). The method follows Hovy and Nirenburg: each pair of concepts is classed as identity, extension or cross-classification. Three principles guide the merge. First, remove distinctions that are not experiential (for example clause versus prepositional phrase), since they only record how a meaning is worded. Second, replace exhaustive cross-classification with an "intelligent" one, dropping cross-classes whose role sets contradict each other (the material-process hierarchy is the worked example: directed actions with a causer, nondirected actions with or without one). Third, let the semantics-grammar interface allow roles to be left unrealised, so a single process concept serves for passives and optional participants. The merged model has 288 concepts. Strong identity covers 41% of the merged concepts, identity after unifying concepts 63%, extension about 25%, cross-classification 3.6%. The merged model is used for sentence generation in English, German and Dutch.

Ideas

I1. An English verb-class table in world: nondirected-doing (agent only: run), nondirected-happening (affected only: wilt), directed-action (causer plus affected: break). Each lexical verb is assigned one class, and the class fixes which role a single argument takes ("The vase broke" against "The child broke the vase").
I2. An intelligent merge rule for overlapping lexical classes: when a verb belongs to two classes whose role sets contradict each other (one requires an agent, the other forbids one), keep the class that matches the parse and log the discarded class.
I3. A removal rule for non-experiential distinctions: two frame entries that differ only in how the event is worded (clause or nominal) must map to one event concept. Count such duplicates in Mova's frame table, and check that the count goes to zero after the merge.
I4. A negative control for the merge: a shuffled verb-to-class assignment must give more role conflicts with the parse than the true table.

For Mova

The chapters are 13. Verbs (transitivity and the causative pattern of break, open, melt), 21. The simple clause (participants and their roles in a clause) and 19. The lexicon: vocabulary, multiword expressions and register (the lexicon and its word senses). The code is world/src/events.rs (event concepts), prag/src/frames.rs (role frames) and en/src/dict.rs (the verb entries). The measures: the number of role conflicts between a verb's class and its parsed frame on the verb list, before and after the intelligent merge, run twice for identical output, and the shuffled-class negative control.

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{henschel1994merged,
  title = {{The Merged Upper Model: A Linguistic Ontology for German and English}},
  author = {Henschel, Renate and Bateman, John},
  booktitle = {COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics},
  year = {1994},
  url = {https://aclanthology.org/C94-2128/}
}

References

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