John Cainoy

Reporters are expected to cover the following subtopics in detail:

• Principle of compositionality: how sentence meaning derives from the meaning of parts and their syntactic arrangement.
• Predicate logic: predicates, arguments, variables, constants, quantifiers, connectives.
• Scope ambiguity and its resolution in natural language.
• Lambda calculus basics for representing functions and argument structures.
• Event semantics (Davidsonian approach) for representing events, thematic roles, and modifiers.
• Applications in formal and applied contexts (e.g., law, computing, AI reasoning).
• Research potential: How formal semantics frameworks can frame empirical studies of sentence meaning and ambiguity.

• Define compositional semantics and explain its role in connecting words to sentence meaning.
• Show how predicate logic serves as a formal language for modeling natural language meaning.
• Introduce how ambiguity in quantification and scope challenges the idea of compositionality.
• Highlight the importance of event semantics in representing complex sentences.

• Heim, I., & Kratzer, A. (2012). Semantics in generative grammar (1st ed.). Blackwell.
• Jacobson, P. (2014). Compositional semantics: An introduction to the syntax–semantics interface. Oxford University Press.
• Szabó, Z. G. (2024). Compositionality. In E. N. Zalta & U. Nodelman (Eds.), The Stanford Encyclopedia of Philosophy (Fall 2024). https://plato.stanford.edu/entries/compositionality/
• Zimmermann, T. E., & Sternefeld, W. (2013). Introduction to Semantics: An Essential Guide to the Composition of Meaning. De Gruyter.

• Compositionality
• Predicate, Argument, Variable, Constant
• Quantifier (∀, ∃)
• Scope Ambiguity
• Logical Connectives (¬, ∧, ∨, →)
• Lambda Calculus
• Event Semantics, Davidsonian Events

• Predicate logic notation for simple and complex sentences.
• Logical Form (LF) representation of quantifier scope.
• Lambda abstraction and functional application.
• Davidsonian event representation with thematic roles (Agent, Theme, Instrument, Location).

• Quantifier Scope Ambiguity:
  “Every student read a book.”
  (∀x)(∃y)[Student(x) ∧ Book(y) ∧ Read(x,y)] vs. (∃y)(∀x)[Book(y) ∧ Student(x) ∧ Read(x,y)].
• Event Semantics:
  “John kissed Mary in the park.”
  ∃e[Kiss(e) ∧ Agent(e, John) ∧ Theme(e, Mary) ∧ Location(e, Park)].
• Lambda Calculus:
  “runs” → λx_Run(x).   “John runs” → Run(John).

• Does compositionality hold for idioms and figurative language?
• How does predicate logic capture (or fail to capture) natural language meaning?
• In what ways do scope ambiguities reflect deeper cognitive or cultural preferences?

• Comparative analysis of quantifier scope preferences across languages.
• Application of event semantics to legal contracts or AI-generated text.
• Lambda calculus as a tool for modeling natural language inference in computational linguistics.