The Proper Treatment of Quantification in Ordinary English

By Richard Montague. 1973

Subfields: Computational Linguistics & Semantics, Philosophy

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Citations in the corpus

Reasoning about Quantities in Natural Language (2015, background): “Montague (Montague, 1973) investigates identity ambiguities in sentences, e.”
Learning Dependency-Based Compositional Semantics (2011, background): “The dominant paradigm, which stems from the seminal work of Richard Montague in the early 1970s (Montague, 1973), states that parts are lambda calculus expressions that correspond to syntactic constituents, and composition is function application.”
Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions (2013, methodology): “To model the meaning of indefinite articles, we depart from the Frege-Montague tradition of using existential quantifiers (Lewis, 1970; Montague, 1973; Barwise & Cooper, 1981), and instead introduce a new quantifier A that, like ι, has type 〈〈e, t〉, e〉.”
Good-Enough Compositional Data Augmentation (2019, background): “This view of compositionality as determined by synchronicity between form and meaning is essentially Montagovian and well-suited to problems in formal semantics (Montague, 1973); however, it requires access to structured meaning representations with explicit types and bracketings, which are not avai”
Probing Natural Language Inference Models through Semantic Fragments (2019, methodology): “The use of semantic fragments has a long tradition in logical semantics, starting with the seminal work of Montague (1973), as well as earlier work on NLI (Cooper et al.”
Learning executable semantic parsers for natural language understanding (2016, background): “Semantic parsing is rooted in formal semantics, pioneered by logician Richard Montague [25], who famously argued that there is “no important theoretical difference between natural languages and the artificial languages of logicians.”
Compositional Operators in Distributional Semantics (2014, background): “The modern compositional models owe a lot to the seminal work of Richard Montague (1930–1971), who has managed to present a systematic way of processing fragments of the English language in order to get semantic representations capturing their ‘meaning’ [30–32].”
On the coherence and structure of discourse (1985, unknown): “(25) force(100 lb, L,Down, x1)& distance(F, x1, 20 ft)& foot(F,L) force(150 lb, x,Down, x2)& distance(T, x2, 10 ft)& top(T, y)”
Wikipedia-based Semantic Interpretation for Natural Language Processing (2009, background): “Early research in computational linguistics focused on deep natural language understanding, and strived to represent text semantics using logical formulae (Montague, 1973).”
Extending Machine Language Models toward Human-Level Language Understanding (2019, background): “Our proposal to focus on language as part of a system representing situations builds on a long tradition in linguistics (35), human cognitive psychology (36), psycholinguistics (37), philosophy (38) and artificial intelligence (39).”
Few-Shot Semantic Parsing for New Predicates (2021, background): “The semantic formalism of meaning representations range from lambda calculas (Montague, 1973), SQL, to abstract meaning representation (Banarescu et al.”
Linguistic side effects (2005, background): “Continuations and the nature of quantification.”
Beyond Word Frequency: Bursts, Lulls, and Scaling in the Temporal Distributions of Words (2009, unknown): “Science 298:1569–1579.”
Neural Compositional Denotational Semantics for Question Answering (2018, background): “Compositionality is a mechanism by which the meanings of complex expressions are systematically determined from the meanings of their parts, and has been widely assumed in the study of both artificial and natural languages (Montague, 1973) as a means for allowing speakers to generalize to understand”