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From Word Types to Tokens and Back: A Survey of Approaches to Word Meaning Representation and Interpretation

Paper · 2023 · Marianna Apidianaki

Summary

This survey examines how vector-based models represent word meaning and how such representations can be improved and interpreted. Its central contrast is between two levels of abstraction. Count-based DSMs and static embeddings (word2vec, GloVe, fastText) assign one vector per word type, merging all of a word's senses. Contextual language models instead produce a separate vector for each occurrence. The author reviews:

  • methods for building type, sense and token embeddings;
  • techniques for specializing vectors with external lexical-semantic knowledge, during or after training;
  • probing and interpretation studies of contextual representations;
  • recent approaches that derive static vectors from contextual ones or merge both kinds.

A key observation is that probing is unreliable for lexical semantics, for two reasons. Model output shifts with small prompt changes, and cloze queries often have several valid answers. In addition, the geometry of contextual vector spaces can distort similarity estimates. These problems have revived interest in type-level vectors and in classic similarity and analogy benchmarks. The survey matters because it places current work alongside earlier paradigms and identifies open problems. Most of the methods and datasets it covers are English-only.

Key points

  • The survey is organized into Sections 2–5:
    • representation paradigms and benchmarks,
    • specialization of embeddings for semantic relations,
    • probing and the geometry of the vector space,
    • type-level vectors from contextual models and static/dynamic hybrids.
  • Static embeddings suffer from "meaning conflation" for polysemous words such as bug. Contextual models handle this by giving each instance its own vector.
  • Multi-SimLex is the main multilingual benchmark covered. It contains 1,888 aligned concept pairs in each of 12 typologically diverse languages.
  • MEN has 3,000 pairs rated by comparative judgments. Each pair was compared against 50 others, which yields a 0–50 scale.
  • The in-context similarity datasets fall into two groups:
    • manually annotated: CoInCo, Usim (1–5 scale) and SCWS (1–10 scale);
    • automatically built: ukWaC-subs (via context2vec substitutes) and WiC (T/F labels derived from WordNet).

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

🎓University of PennsylvaniaPhiladelphia · United States233 works · 65,583 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 works

🧭 Topics 2Research subjects and languages the work is about.Semantic parsing & meaning representation · 193 worksLexical semantics & word embeddings · 65 works

Cite

Cite as Marianna Apidianaki. 2023. From Word Types to Tokens and Back: A Survey of Approaches to Word Meaning Representation and Interpretation. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00474

@article{apidianaki2023word,
  title = {{From Word Types to Tokens and Back: A Survey of Approaches to Word Meaning Representation and Interpretation}},
  author = {Marianna Apidianaki},
  journal = {Computational Linguistics},
  year = {2023},
  doi = {10.1162/coli\_a\_00474},
  url = {https://doi.org/10.1162/coli_a_00474}
}

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