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Assessing the Productivity of Old English –læcan

Paper · 2012 · Gema Maíz Villalta

Summary

Old English word-formation has been described in depth, but the productivity of individual affixes has rarely been measured. This is hard for a dead language: no speakers can be tested for new coinages, and the record covers about six centuries. Maíz Villalta studies the verbal suffix -læcan.

The analysis has two parts. The first is qualitative. She argues that the suffix grammaticalized out of the free weak verb lǣcan 'to spring up', itself derived from strong lācan. Its meaning moved from literal motion to abstract internal aspect: mostly inchoative on noun bases, mostly causative on adjective bases. Meanwhile, some individual derivatives drifted into lexicalized, non-compositional senses.

The second part is quantitative. She applies Baayen's productivity formulae, taking type frequency from the Nerthus lexical database and token frequency from The Dictionary of Old English Corpus.

The method is the main contribution: it combines dictionary and corpus evidence and treats grammaticalization and lexicalization as gradients. Our excerpt lacked the numerical section, so no figures are recorded here.

Key points

  • -læcan attaches to nouns, adjectives, verbs (limplǣcan < gelimpan) and adverbs (gesamodlǣcan < samod).
  • Base category and function:
    • Noun bases mostly give inchoative verbs (winterlǣcan, sumorlǣcan, ǣfenlǣcan). Causative exceptions are gewundorlǣcan and gehīwlǣcan.
    • Adjective bases are often causative ((ge)rihtlǣcan, fremedlǣcan). Exceptions include geonglǣcan and (ge)ðrīstlǣcan.
  • Data sources:
    • Nerthus has ca. 30,000 headwords, drawn mainly from Clark Hall and secondarily from Bosworth–Toller and Sweet.
    • The DOE Corpus has ca. 3,060 texts (prose, poetry, glosses, inscriptions).
  • At least eight derivatives are flagged as partly lexicalized, e.g. swæðlǣcan 'search for' < swæð 'track' and wiðerlǣcan 'deprive' < wiðer 'hostile'.
  • The paper adopts Fischer's (2008) criterion: lexicalization works at token level, grammaticalization at both token and type level. It also takes Kastovsky's three obstacles to measuring historical productivity as its starting point.

Interesting ideas

  • I1. Type frequency is taken from a dictionary-based database and token frequency from a running-text corpus, so each kind of source supplies the measure it is reliable for.
  • I2. The category of the base largely predicts the derived verb's aspectual meaning: nouns lean inchoative, adjectives lean causative, and exceptions are listed explicitly.
  • I3. A derivative counts as lexicalized when its meaning cannot be computed from base plus affix, and lexicalization is treated as a scale rather than a yes/no label.
  • I4. Fischer's test asks whether a change affects the whole pattern (type level) or only particular words (token level).
  • I5. A free word and a homophonous bound affix coexisting in the lexicon is read as evidence of ongoing grammaticalization, not as noise.

For Mova

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

  • I1 (learn): when estimating how general a construction or rule is, count distinct lemmas from a lexicon separately from occurrences in silver data, so one frequent item cannot fake generality.
  • I2 (frames): derived verbs (e.g. -en, -ify) could get a default event frame (become-X vs make-X) from the base's POS, with an exception list overriding it.
  • I3 (world): mark non-compositional derivatives in the compiled lexicon with a compositionality degree, so the world builder does not decompose them into base-concept + change-of-state.
  • I4 (learn): self-induced repair rules could be classified the same way, promoting a fix to a general rule only when it holds across many lemmas and otherwise storing it as a per-word exception.
  • I5 (tokens): items like "-like" or "-wise", which are both free words and suffixes, could get dual analyses in the morphology module instead of being forced into one.

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 · Spain13 works · 74 citations

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 2Research subjects and languages the work is about.English · 1,373 worksHistorical linguistics & language change · 159 works

Cite

Cite as Gema Maíz Villalta. 2012. Assessing the Productivity of Old English –læcan. Miscelánea: A Journal of English and American Studies 43.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.26754/ojs_misc/mj.20119283

@article{villalta2012assessing,
  title = {{Assessing the Productivity of Old English –læcan}},
  author = {Gema Maíz Villalta},
  journal = {Miscelánea: A Journal of English and American Studies 43},
  year = {2012},
  doi = {10.26754/ojs\_misc/mj.20119283},
  url = {https://doi.org/10.26754/ojs_misc/mj.20119283}
}

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