Compositionality

By T. Janssen, Barbara H. Partee. 1997

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Citations in the corpus

Taskonomy: Disentangling Task Transfer Learning (2018, background): “compositional modeling [33, 8, 11, 21, 53, 89, 87], homomorphic cryptography [40], lifelong learning [90, 13, 82, 81], functional maps [68], certain aspects of Bayesian inference and Dirichlet processes [52, 88, 87, 86, 35, 37], few-shot learning [78, 23, 22, 67, 83], transfer learning [72, 81, 27, ”
Recursive Neural Networks Can Learn Logical Semantics (2014, background): “…limit the scope of our experiments in this paper to neural network models that adhere to the linguistic principle of compositionality, which says that the meanings for complex expressions are derived from the meanings of their parts via specific composition functions (Partee, 1984; Janssen, 1997).”
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem? (2023, background): “The principle of compositionality broadly states that the semantics of a complex expression are determined by its structure and the semantics of its constituents [49].”
Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space (2010, background): “…it has been limited to the level of content words (nouns, adjectives, verbs), and it hasn’t tackled in a general way compositionality (Frege, 1892; Partee, 2004), that crucial property of natural language which allows speakers to derive the meaning of a complex linguistic constituent from the…”
Compositionality I: Definitions and Variants (2010, background): “Cf. Janssen 1997, Westerst̊ahl 2009.”
Compositional Generalization by Learning Analytical Expressions (2020, background): “…formally, compositionality states such a phenomena, where the meaning of a complex expression (e.g. a sentence) is determined by the meanings of its constituents (e.g. the verb “jump” and the adverb “twice") together with the way these constituents are combined (e.g. an adverb modiﬁes a verb) [35].”
Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both? (2020, background): “These formalisms directly embody the formal notion of the principle of compositionality as a homomorphism between the parts of the source and target structures, and operations over these parts (Montague, 1970; Janssen and Partee, 1997).”
Image Parsing: Unifying Segmentation, Detection, and Recognition (2003, unknown): “Instead, the parsing subgraphs should be built by grouping small elements in a “compositional” manner [5].”
50 Years of object recognition: Directions forward (2013, background): “An often encountered argument in linguistics, refers to the need to use a sparse set of word representations in any given language, as a means of ensuring redundancy and efficient communication, despite the existence of potentially ambiguous basic speech signals [204, 205].”
Recursive Neural Networks for Learning Logical Semantics (2014, methodology): “We study neural models that adhere to the linguistic principle of compositionality , which says that the meanings for complex expressions are derived from the meanings of their constituent parts via speciﬁc composition functions [19, 20].”
Semantics and the electrophysiology of meaning: Tense, aspect, event structure (2004, background): “Thus, even if systematicity held, it would not buy us compositionality (see Johnson [104], Szabó[198], and Pullum and Scholz [166] for a discussion).”