A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces
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Abstract (arXiv)
We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive semantic characterizations of lexical items in two grammatical constructions: nouns in subject or object position within the same sentence, and the AANN construction (e.g., `a beautiful three days'). We show that a word in subject position is interpreted as more agentive than the very same word in object position, and that the nouns in the AANN construction are interpreted as more measurement-like than when in the canonical alternation. Our method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 18 of its 62 references that are in the map. First posted on arXiv 29 May 2023.
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18 of 62 references.
References · 18
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018Attention-based Deep Multiple Instance Learningcited by 1,077
- 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
- 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
- 2020A Primer in BERTology: What we know about how BERT workscited by 807
- 2018WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representationscited by 370
- 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 350
- 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 137
- 2020Analysing Lexical Semantic Change with Contextualised Word Representationscited by 92
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- 2021On the proper role of linguistically-oriented deep net analysis in linguistic theorizingcited by 43
- 2021Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERTcited by 41
6 more
- 2022When classifying grammatical role, BERT doesn't care about word order... except when it matterscited by 24
- 2023A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Constructioncited by 22
- 2020(Re)construing Meaning in NLPcited by 16
- 2020Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddingscited by 12
- 2021ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicalitycited by 12
- 2019Feature2Vec: Distributional semantic modelling of human property knowledgecited by 4
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Citing papers per year: 2023: 1; 2024: 3; 2025: 8; 2026: 1.
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- 2023Visual Grounding Helps Learn Word Meanings in Low-Data Regimescited by 16
- 2024Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasonscited by 13
- 2024Lexicon-Level Contrastive Visual-Grounding Improves Language Modelingcited by 6
- 2025Constructions are Revealed in Word Distributionscited by 6
- 2025Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructionscited by 5
- 2025Construction Identification and Disambiguation Using BERT: A Case Study of NPNcited by 4
- 2024Analysis of Argument Structure Constructions in the Large Language Model BERTcited by 3
- 2025Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Eracited by 3
- 2025Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddingscited by 2
- 2025semantic-features: A User-Friendly Tool for Studying Contextual Word Embeddings in Interpretable Semantic Spacescited by 1
- 2025Meaning-infused grammar: Gradient Acceptability Shapes the Geometric Representations of Constructions in LLMscited by 1
- 2025LLMs Learn Constructions That Humans Do Not Knowcited by 1
- 2026Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions29 May 2026
- 2025LLMs Learn Constructions That Humans Do Not Know22 Aug 2025
- 2025Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings19 Aug 2025
- 2025Meaning-infused grammar: Gradient Acceptability Shapes the Geometric Representations of Constructions in LLMs29 Jul 2025
- 2025semantic-features: A User-Friendly Tool for Studying Contextual Word Embeddings in Interpretable Semantic Spaces6 Jun 2025
- 2025Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Era4 Jun 2025
- 2025Construction Identification and Disambiguation Using BERT: A Case Study of NPN24 Mar 2025
- 2025Constructions are Revealed in Word Distributions8 Mar 2025
- 2025Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions8 Jan 2025
- 2024Analysis of Argument Structure Constructions in the Large Language Model BERT8 Aug 2024
- 2024Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons26 Mar 2024
- 2024Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling21 Mar 2024
1 more
- 2023Visual Grounding Helps Learn Word Meanings in Low-Data Regimescited by 16
- 2024Lexicon-Level Contrastive Visual-Grounding Improves Language Modelingcited by 6
- 2025Constructions are Revealed in Word Distributionscited by 6
- 2025Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructionscited by 5
- 2025Construction Identification and Disambiguation Using BERT: A Case Study of NPNcited by 4
- 2024Analysis of Argument Structure Constructions in the Large Language Model BERTcited by 3
- 2025Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Eracited by 3
- 2025Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddingscited by 2
- 2025semantic-features: A User-Friendly Tool for Studying Contextual Word Embeddings in Interpretable Semantic Spacescited by 1
- 2025Meaning-infused grammar: Gradient Acceptability Shapes the Geometric Representations of Constructions in LLMscited by 1
- 2025LLMs Learn Constructions That Humans Do Not Knowcited by 1
- 2026Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructionscited by 0
- 2026Language Models Learn Constructional Semantics, Not To Mention Syntax: Investigating LM Understanding of Paired-Focus Constructions29 May 2026
- 2025LLMs Learn Constructions That Humans Do Not Know22 Aug 2025
- 2025Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings19 Aug 2025
- 2025Meaning-infused grammar: Gradient Acceptability Shapes the Geometric Representations of Constructions in LLMs29 Jul 2025
- 2025semantic-features: A User-Friendly Tool for Studying Contextual Word Embeddings in Interpretable Semantic Spaces6 Jun 2025
- 2025Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Era4 Jun 2025
- 2025Construction Identification and Disambiguation Using BERT: A Case Study of NPN24 Mar 2025
- 2025Constructions are Revealed in Word Distributions8 Mar 2025
- 2025Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions8 Jan 2025
- 2024Analysis of Argument Structure Constructions in the Large Language Model BERT8 Aug 2024
- 2024Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling21 Mar 2024
- 2023Visual Grounding Helps Learn Word Meanings in Low-Data Regimes20 Oct 2023