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The Lefff, a Freely Available and Large-coverage Morphological and Syntactic Lexicon for French

Summary

The paper presents the Lefff (version 3.0.1), a French lexicon covering both morphology and syntax. It is meant to meet four requirements together: broad coverage, high quality, direct usability in NLP software, and free availability. The author argues that no earlier French resource, including Lexicon-Grammar tables, Dicovalence and Les Verbes Français, satisfies all four. The lexicon is built in Alexina, a framework that does not depend on a particular language or grammar formalism and is compatible with LMF. Alexina keeps a compact intensional lexicon for development and compiles it automatically into an extensional lexicon that lists every inflected form. In the syntactic layer, subcategorization frames are expressed with initial syntactic functions, and constructions such as the passive or impersonal are derived through redistributions. The lexicon's data come from semi-automatic acquisition and from converting and merging existing resources. The evaluation covers lemma counts against other lexicons, a POS tagger, and a deep parser. Adding the Lefff improves tagging, and the parser performs better with Lefff verb entries than with converted Lexicon-Grammar entries. The resource matters because many French parsers built on different formalisms depend on it.

Key points

  • The Lefff is released under LGPL-LR. Alexina lexicons also exist or are in progress for Spanish (Leffe), Galician, Polish, Slovak, Persian and Sorani Kurdish, with English planned. Morph-it! (Italian) and the Alpino lexicon (Dutch) have been converted into the format.
  • Lemma counts are 6,825 verbs, 37,530 nouns, 10,483 adjectives, 3,584 adverbs and 225 prepositions. The paper compares these with Morphalou, Multext and Dicovalence, noting that Morphalou has more lemmas partly because it counts masculine and feminine nouns separately.
  • A maximum-entropy POS tagger trained on the French TreeBank reaches 97.0% accuracy (86.1% on unknown words). With Lefff-based features it reaches 97.7% (90.1% on unknown words). For a given accuracy level, the full lexicon cuts the training data needed by at least half and up to two thirds.
  • In the FRMG (TAG-based) parser, swapping Lefff verb entries for converted Lexicon-Grammar entries lowers the EASy "relations" f-measure from 59.9% to 56.6%.
  • Redistributions defined for French verbs include %actif, %passif, %passif de, %impersonnel, %se moyen and their impersonal variants.

Interesting ideas

  • I1. Separate the lexicon you edit from the lexicon tools use. Linguists maintain a compact, lexeme-level description, and a compiler produces the full form-level lexicon. For a highly inflected language such as Ukrainian, this keeps manual work small while still giving tools a ready lookup table.
  • I2. Use one-step redistributions instead of chained lexical rules. Constructions like the passive map initial functions to final ones exactly once, so termination problems cannot arise. Complex cases can still be defined as a composition of two redistributions. This design is useful in any rule system where iterated rules could loop or overgenerate.
  • I3. Use the lexicon to tell the tagger about upcoming words. A lexicon can list the possible tags of tokens to the right of the current word, which training data alone cannot supply for unseen words. Adding this as features is a cheap improvement for low-resource taggers.
  • I4. Simulate lexicon growth by keeping only the most frequent lemmas. Measuring downstream gains at each frequency cutoff shows how much lexicon work pays off and how much annotated data it can replace. This helps decide whether to spend effort on lexicons or on treebank annotation.
  • I5. More entries can hurt. Adding rare or archaic words, or listing more verb arguments, increases ambiguity and parsing time and can lower accuracy. Testing a lexicon by swapping it inside a fixed parser shows its real usefulness better than counting entries.

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

Fields: Linguistics & languages

Topics: Part-of-speech tagging

Cited by

78 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 74 not yet.

Cite

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

@inproceedings{sagot2010lefff,
  title = {{The Lefff, a Freely Available and Large-coverage Morphological and Syntactic Lexicon for French}},
  author = {Sagot, Benoît},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  doi = {10.63317/5o4muwtyahd8},
  url = {https://doi.org/10.63317/5o4muwtyahd8}
}

References

38 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  21. Benoı̂t Sagot and Éric de La Clergerie. 2006. Error mining in parsing results. In Proceedings of ACL/COLING 2006, pages 329–336, Sydney, Australia. find
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