Semi-supervised learning of morphological paradigms and lexicons

What it is about

The paper turns inflection tables into abstract paradigms, by replacing every common substring of a table with a variable. The result stays readable, so a linguist can inspect and correct it. Paradigms are then used to inflect unseen base forms, and to collect lemmas from unannotated text. The evaluation uses Wiktionary tables for German, Spanish and Finnish: without corpus information, per-form accuracy on unseen base forms runs from 87.81% (German nouns) to 99.52% (Spanish verbs). With a Wikipedia-based frequency tie-break it runs from 91.81% to 99.58%. In a Swedish lexicon task, the first 100 collected words are 100.0% correct and the first 1000 are 96.4% correct. On the Durrett and DeNero (2013) tables, the number of output paradigms is below 10% of the number of input tables in every case.

Ideas

• I1. en/src/morph.rs: generalise the English inflection tables from data/forms.tsv into maximally general paradigms (common substrings as variables) and count the resulting paradigms against the lemma count. Measure the per-form accuracy on held-out English lemmas with a two-fold split, and report it with en folds.
• I2. Tie-break by frequency: when two paradigms fit one lemma, prefer the one that generates more attested forms in the school silver corpus. Measure the gain against a random tie-break on EWT test, as the negative control.
• I3. Lexicon collection probe: take a list of English nouns from raw text, rank them by how well they fit the induced paradigms, and report the precision at 100 and 1000 words, as the paper does for Swedish. The precision check goes into en lexicon.

For Mova

The code is en/src/morph.rs and en/src/lexgen.rs, with en lexicon and en morph-eval for checks. The chapters are 11. Nouns and 14-irregular-verbs.md, where the paradigm view fits best. The paper's tables are Wiktionary-derived and its Swedish list is not English, so use it for ideas and tests only. Measure first: per-form accuracy on held-out English lemmas, two runs identical, before any claim.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.3115/v1/e14-1060

@inproceedings{hulden2014semi,
  title = {{Semi-supervised learning of morphological paradigms and lexicons}},
  author = {Hulden, Mans and Forsberg, Markus and Ahlberg, Malin},
  booktitle = {EACL 2014 (Gothenburg)},
  year = {2014},
  doi = {10.3115/v1/e14-1060},
  url = {https://doi.org/10.3115/v1/e14-1060}
}

References

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