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Literal and Metaphorical Sense Identification through Concrete and Abstract Context

Summary

The paper addresses telling apart literal and metaphorical uses of a word in context, which matters for textual entailment and the applications built on it. Earlier work treated this as standard word sense disambiguation. The authors instead draw on cognitive linguistics, where metaphor carries knowledge from concrete source domains into abstract target domains. Their hypothesis is that the more abstract a word's surroundings are, the more likely the word is used metaphorically.

They first build an automatic abstractness scorer. Each word is compared, through an LSA-style vector space built from a large web corpus, with sets of abstract and concrete "paradigm" words, and the paradigm words are selected using MRC Psycholinguistic Database ratings. They then turn the abstractness of context words into features for logistic regression.

Tests on adjective–noun phrases and on the TroFi Example Base give state-of-the-art or comparable results. Because the features describe only the context, the classifier also works on verbs it never saw in training.

Key points

  • Abstractness scorer:
    • Score = summed similarity to 20 abstract paradigm words minus summed similarity to 20 concrete ones, rescaled to the 0–1 range.
    • Trained on half of the 4,295 MRC-rated words and tested on the other half, where it reaches a correlation of 0.81 with the human ratings.
  • Semantic space:
    • About 5×10¹⁰ words of university web text, indexed with Wumpus.
    • Vocabulary of 114,501 WordNet terms.
    • Word–context matrix weighted with PPMI and reduced with truncated SVD, following Rapp's approach.
  • Adjective experiment: 100 adjective–noun phrases labeled denotative or connotative by five annotators. The classifier reaches 79% average accuracy (e.g., deep snow vs. deep appreciation).
  • TroFi experiments: 50 verbs in 3,737 sentences from the 1987–89 WSJ corpus.
    • With one model per verb (Birke and Sarkar's setup), average f-score is 63.9%, versus their 64.9%.
    • With a single model trained on 25 new verbs and tested on the 25 original, unseen verbs, average f-score is 68.1%.
  • Feature weights: In the logistic regression, average noun abstractness (excluding proper nouns) has by far the largest weight, about 11.4. Adding the target word's own abstractness gave no significant gain, probably because the targets span a narrow abstractness range.

Interesting ideas

  • I1. Paradigm-word scoring: A word can be rated on any semantic scale by comparing it with two small sets of anchor words. Here the anchors are picked with a supervised method from a human-rated lexicon rather than chosen by hand. The same recipe could produce lexicons for other dimensions or other languages, as long as some seed ratings exist.
  • I2. Context-only features: Describing only the context, and never the target word, makes the classifier independent of the target. One model can then cover unseen words, which saves collecting labeled data for every word of interest.
  • I3. Unseen-word evaluation: Training on one set of verbs and testing on a disjoint set is a demanding check of generalization. It is worth copying wherever a method claims to transfer across lexical items.
  • I4. Theory-driven features: A cognitive-linguistics claim (concrete-to-abstract transfer) is turned into a small set of interpretable features. The regression weights then show which part of speech in the context carries the signal, here mainly nouns.
  • I5. Swapping the rating source: The authors note the same scorer could be trained on MRC imagability ratings instead of abstractness. This could serve tasks such as image retrieval or image annotation, showing how one rating pipeline can be reused for different psycholinguistic properties.

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: Word sense disambiguation

Cited by

156 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 149 not yet.

Cite

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

@inproceedings{turney2011literal,
  title = {{Literal and Metaphorical Sense Identification through Concrete and Abstract Context}},
  author = {Turney, Peter and Neuman, Yair and Assaf, Dan and Cohen, Yohai},
  booktitle = {Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing},
  year = {2011},
  url = {https://aclanthology.org/D11-1063/}
}

References

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