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Patterns in CAOSS: Distributed representations predict variation in relational interpretations for familiar and novel
Fritz Günther1, Marco Marelli2
1Humboldt-Universität zu Berlin, Berlin, Germany.
Cognitive Psychology
|March 27, 2022
Summary
Distributional semantic models capture word meanings. This study shows these models can predict varied interpretations of compound words, demonstrating their interpretability and ability to represent nuanced meaning.
Area of Science:
- Computational Linguistics
- Cognitive Science
- Natural Language Processing
Background:
- Distributional semantic models (DSMs) represent word meanings as vectors from text corpora.
- DSMs are criticized for lack of interpretability and inability to capture meaning variations.
Purpose of the Study:
- To demonstrate that semantic vectors can capture and predict nuanced interpretations of compound words.
- To address criticisms regarding DSM interpretability and meaning variation.
Main Methods:
- Trained a linear regression model to predict inter-individual variation in relational interpretations of compound words.
- Used compositional semantic vectors representing compound word meanings as input.
- Tested on both familiar (moon light) and novel (wood brush) compounds.
Main Results:
- The mapping system successfully predicted relational interpretations of compound words.
- Predictions significantly outperformed random baselines.
- Demonstrated that semantic vectors encode qualitative interpretation variations.
Conclusions:
- Distributional semantic vectors encode interpretable variations in meaning.
- Simple linear regression can decode these nuanced interpretations.
- DSMs possess greater capacity for representing meaning variation than previously assumed.
Keywords:
Compound wordsConceptual combinationDistributional semanticsNovel compoundsRelational informationMore Related Videos
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