mlsanityData to 7 Oct 2026

A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces

arXiv 2305.18598 ↗First posted 29 May 2023cs.CLcited by 13

Not on the map. Why?

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.

Builds on

18 of 62 references.

References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  3. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  4. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  5. 2020A Primer in BERTology: What we know about how BERT workscited by 807
  6. 2018WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representationscited by 370
  7. 2016Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependenciescited by 350
  8. 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 137
  9. 2020Analysing Lexical Semantic Change with Contextualised Word Representationscited by 92
  10. 2018What do RNN Language Models Learn about Filler-Gap Dependencies?cited by 86
  11. 2021On the proper role of linguistically-oriented deep net analysis in linguistic theorizingcited by 43
  12. 2021Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERTcited by 41
6 more
  1. 2022When classifying grammatical role, BERT doesn't care about word order... except when it matterscited by 24
  2. 2023A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Constructioncited by 22
  3. 2020(Re)construing Meaning in NLPcited by 16
  4. 2020Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddingscited by 12
  5. 2021ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicalitycited by 12
  6. 2019Feature2Vec: Distributional semantic modelling of human property knowledgecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 8; 2026: 1.

13 papers.

Sort:

By area: