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Experiments on the Automatic Induction of German Semantic Verb Classes

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" does not allow redistributing the text itself. Original — aclanthology.org/J06-2001.pdf

Summary

The work addresses the absence of a German counterpart to Levin-style verb classifications, which are useful for coping with data sparseness in NLP. It tests whether semantic verb classes can be induced automatically by exploiting the linguistic assumption that a verb's meaning partly determines its behaviour, especially its argument realisation and diathesis alternations. A statistical grammar model of German, trained on a newspaper corpus, supplies distributional verb descriptions at three increasingly rich levels: syntactic frames, prepositional information, and selectional preferences of arguments. These profiles are grouped with the hard clustering algorithm k-means. The resulting clusters are compared against a hand-built gold standard using several evaluation measures. The goals are to measure how far the meaning–behaviour link actually holds in practice and to determine which clustering parameters suit this linguistic task. The method is tuned on a small verb set and then scaled up to 883 verbs. The study matters as a systematic account of both the potential and the limits of cheap corpus data combined with a simple algorithm for building German lexical-semantic resources.

Key points

  • The gold standard contains 168 German verbs in 43 fine-grained semantic classes. Classes hold 2–7 verbs (3.9 on average), and 8 verbs are ambiguous.
  • Verb frequencies span 8 to 71,604 in a 35-million-word German newspaper corpus. This deliberately tests the clustering on both data-rich and data-poor cases.
  • The syntactic level uses 38 subcategorization frame types with up to three arguments. Arguments include NP cases, reflexives, PPs, expletive es, and several clause types.
  • Clustering uses k-means (Forgy 1965). The setup is developed on the small set before being applied to a large-scale set of 883 verbs.
  • Suggested future uses include soft clustering and applying the classes in NLP systems such as class-based language models.

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: Natural language processing

Cited by

56 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 55 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli.2006.32.2.159

@article{sabine2006experiments,
  title = {{Experiments on the Automatic Induction of German Semantic Verb Classes}},
  author = {Sabine, Schulte im Walde,},
  journal = {Computational Linguistics},
  year = {2006},
  doi = {10.1162/coli.2006.32.2.159},
  url = {https://doi.org/10.1162/coli.2006.32.2.159}
}

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