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The Semantic Map of "Aktionart" and Lexical Entailment of Old English Strong Verbs

Paper · 2022 · Luisa Fidalgo Allo

Summary

The paper looks at how Old English derived verbs relate in meaning to the primitive strong verbs they come from. It combines two dimensions: lexical entailment and Aktionsart (inner temporal aspect). The data are the derivatives in all 328 Old English lexical paradigms built on strong verbs. Each pair of meanings (synsets) is labelled with a WordNet-style relation: troponymy, -troponymy, backward presupposition, cause, plus synonymy and opposition. Every meaning is also assigned an Aktionsart class, using the classification set out by Van Valin (2014) and earlier by Van Valin and LaPolla (1997).

The counts are combined into one hierarchical connectivity map. In it, the area given to each relation reflects how often it occurs, and each area is split by whether the paired synsets share their Aktionsart. The analysis shows systematic links between entailment type and aspectual class.

The author's main methodological claim is that semantic maps are more flexible than usually assumed. They can describe a single language, a historical language and individual lexical items, not only cross-linguistic grammatical categories.

Key points

  • Coverage: derivatives from all 328 Old English lexical paradigms headed by strong verbs. Paradigms are analysed stepwise (one affix per step) and panchronically.
  • Relation inventory: WordNet entailment types (troponymy, -troponymy, backward presupposition, cause) extended with synonymy and opposition. Aktionsart follows Role and Reference Grammar classes (state, achievement, semelfactive, accomplishment, activity, active accomplishment, causatives).
  • Entailing synsets split nearly evenly: 1,110 spontaneous vs 1,129 causative. Entailed synsets lean spontaneous: 1,387 vs 852.
  • When troponym pairs differ in Aktionsart, the most frequent cause is meaning specification and the least frequent is figurative association. Opposition pairs typically involve state or change-of-state classes. -Troponymy pairs contain at least one activity.
  • Map layout:
    • Relations with temporal inclusion (troponymy, -troponymy) sit on the left.
    • Relations without temporal inclusion (backward presupposition, cause) sit in the centre.
    • Mutually entailing relations (synonymy, opposition) sit on the right.

Interesting ideas

  • I1. Label each verb-pair relation with both an entailment type and the aspect classes of the two members; aspect mismatch then becomes a diagnostic of what the derivation adds.
  • I2. Group entailment relations by whether one event temporally includes the other, separating them from cause/presupposition and from mutual entailment.
  • I3. Tracking whether the entailing verb is causative and the entailed one spontaneous shows a systematic shift: causatives often entail a spontaneous result.
  • I4. Build a hierarchical map whose areas encode relation frequency, split by a binary property such as shared vs non-shared aspect, to see the whole lexicon at a glance.
  • I5. Treat derivational families stepwise, one affix per step, so every derived meaning has an explicit chain back to its base.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): The compiled verb-class layer could store entailment edges between verbs together with their Aktionsart, so inferring "X V1 ⇒ X V2" also carries the right change-of-state or ongoing-activity consequences.
  • I2 (world): Event ordering could treat troponym-style entailments as simultaneous, presupposition as an earlier event, and cause as a precondition, giving explainable timeline inferences.
  • I3 (frames): When a causative frame is detected, the parser could add a derived spontaneous-event frame for the undergoer, with its role mapping recorded for explanation.
  • I4 (learn): A similar proportional diagram over silver-data verb relations could reveal which relation and aspect combinations are rare or suspicious and deserve LLM-teacher review.
  • I5 (tokens): The lemmatizer could record affix-by-affix derivation chains for English derived words, making morphology-based meaning guesses traceable.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Universidad de La RiojaLogroño · Spain24 works · 125 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,379 worksHistorical linguistics & language change · 250 works

Cite

Cite as Luisa Fidalgo Allo. 2022. The Semantic Map of "Aktionart" and Lexical Entailment of Old English Strong Verbs. Journal of English Studies 20.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18172/jes.5185

@article{allo2022semantic,
  title = {{The Semantic Map of "Aktionart" and Lexical Entailment of Old English Strong Verbs}},
  author = {Luisa Fidalgo Allo},
  journal = {Journal of English Studies 20},
  year = {2022},
  doi = {10.18172/jes.5185},
  url = {https://doi.org/10.18172/jes.5185}
}

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