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Hapax Legomena and the Productivity of the Old English Weak Verb Suffixes 1

Paper · 2014 · Roberto Torre Alonso

Summary

The article examines how productive seven Old English suffixes that build weak verbs (-ettan, -læcan, -sian, -nian, -lian, -erian, -cian) were, viewed synchronically. A historical language makes this hard: the records are fragmentary, productivity cannot be tested on speakers, and the period spans centuries of change. Torre Alonso and Maíz Villalta take types from the Nerthus lexical database and tokens from The Dictionary of Old English Corpus. They compute frequency by type and by token, along with two Baayen-style measures: a narrowly defined productivity index (P) and a global one (P*). Their methodological contribution is to stop treating "hapax" as a single category. They separate three kinds: absolute hapaxes (one occurrence, one text), relative hapaxes (several texts, once in each) and mixed hapaxes (both kinds together), and compute productivity under each. This graded view brings out -sian, -erian, -lian and -cian, whose productivity runs from very low to zero. The authors argue that the method measures low productivity more accurately and fits better with qualitative evidence of lexicalisation and affix loss.

Key points

  • Seven weak-verb suffixes are studied. Earlier grammars (Jember et al. 1975, Kastovsky 1992, Lass 1994, Quirk and Wrenn 1994) disagree on the inventory, and only -ettan and -sian are accepted by all. The authors therefore argue for the derivational status of the other five.
  • Token data come from The Dictionary of Old English Corpus: about 3 million words in roughly 3,000 texts, split into prose, poetry and glosses.
  • Type data come from Nerthus (consulted May 2011), which has about 30,000 headwords drawn from Bosworth-Toller, Sweet and Clark Hall.
  • The measures are type and token frequency, P (hapaxes divided by tokens, after Baayen) and global productivity P*. Each is computed under three hapax definitions: absolute, relative and mixed.
  • Following Bauer, productivity is split into availability and profitability. The authors argue that profitability, meaning how much a process is actually used, has to be central for a historical language.

Interesting ideas

  • I1. Count singletons at more than one granularity: once in the whole corpus versus once per document. This separates truly isolated forms from forms spread thinly across sources, which a single hapax count merges.
  • I2. Pair a lemmatised lexicon for types with an unlemmatised corpus for tokens. One dictionary type then maps to many inflected forms, each with its own text frequency.
  • I3. Low scores may reflect weak measurement rather than low productivity, so check them against qualitative evidence such as lexicalisation or loss of semantic transparency.
  • I4. Random-permutation tools (Suomela's Types 1/Types 2) give upper and lower bounds for type counts at chosen significance levels, so comparisons between subcorpora become tests rather than raw numbers.
  • I5. When reference sources disagree on an inventory, argue the status of each disputed item from evidence before counting anything.

For Mova

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

  • I1 (learn): when judging silver data, a new lemma or construction seen once in each of several documents can count as better evidence of a live pattern than a lone corpus-wide singleton.
  • I2 (tokens): the morphology module could weigh derivational analyses of unknown words using WordNet lemma types together with corpus token counts per inflected form.
  • I3 (learn): low-count metrics for rare classes should be flagged as uncertain and cross-checked with negative controls, not taken at face value.
  • I4 (learn): permutation bounds could show whether a construction or error type is genuinely more frequent in one genre, such as fables versus school texts, or whether the difference is noise.
  • I5 (tokens): disputed derivational affixes in the English morphology tables could carry a recorded justification, which keeps lemma splitting explainable.

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

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,263 worksHistorical linguistics & language change · 249 works

Cite

Cite as Roberto Torre Alonso. 2014. Hapax Legomena and the Productivity of the Old English Weak Verb Suffixes 1. NJES: Nordic Journal of English studies 13.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.35360/njes.325

@article{alonso2014hapax,
  title = {{Hapax Legomena and the Productivity of the Old English Weak Verb Suffixes 1}},
  author = {Roberto Torre Alonso},
  journal = {NJES: Nordic Journal of English studies 13},
  year = {2014},
  doi = {10.35360/njes.325},
  url = {https://doi.org/10.35360/njes.325}
}

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