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Near-Synonymy and Lexical Choice

Summary

Edmonds and Hirst address how a computer can pick the most fitting word among near-synonyms such as error, mistake, blunder and slip. These words share a basic sense but differ in nuance, so the choice matters for machine translation and text generation. The authors argue that earlier lexicon models fail to capture these fine distinctions. They propose a theory built on granularity of representation: a word's meaning combines a shared, context-free core with explicit contrasts against its neighbours, shaped by context. On this basis they design a clustered model of lexical knowledge. A conventional ontology is stopped at a coarse level, and each ontology node links to a cluster of near-synonyms. The cluster encodes how its members differ in denotation, implication, attitude and style. A two-level lexical-choice procedure first selects clusters and then words within them. It matches inputs approximately against weighted preferences. The approach is implemented in the prototype I-Saurus. It matters because it gives a computable way to handle nuance without filling the ontology with language-specific concepts.

Key points

  • The fine-grained distinctions used as examples come from two synonym dictionaries: Webster's New Dictionary of Synonyms (Gove 1984), with roughly 9,000 words arranged in 1,800 sets, and Hayakawa's (1994) Choose the Right Word, with roughly 6,000 words arranged in 1,000 sets.
  • Near-synonyms are grouped into subconceptual clusters attached to a coarse-grained ontology. Each cluster distinguishes its members along denotation, implication, expressed attitude and style, and the model can extend to other variation such as collocations.
  • Lexical-choice criteria are formalised as preferences: conveying concepts with varying degrees of indirectness, expressing attitudes, and achieving particular styles.
  • The choice algorithm works on two tiers, first between clusters and then among near-synonyms within a cluster, using approximate matching of input to lexical representations.
  • The system is realised in I-Saurus, a sentence-planning prototype. The authors present its behaviour through worked examples and frame it as groundwork for MT, NLG and an "intelligent thesaurus" that ranks word options.

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

Topics Lexical semantics & word embeddings

 Institutions

🎓University of TorontoToronto · Canada40 works · 49,261 citations🏢Sharp Laboratories of EuropeOxford · United Kingdom2 works · 412 citations

Cite

Cite as Philip Edmonds and Graeme Hirst. 2002. Near-Synonymy and Lexical Choice. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/089120102760173625

@article{edmonds2002near,
  title = {{Near-Synonymy and Lexical Choice}},
  author = {Philip Edmonds and Graeme Hirst},
  journal = {Computational Linguistics},
  year = {2002},
  doi = {10.1162/089120102760173625},
  url = {https://doi.org/10.1162/089120102760173625}
}

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