ﻻ يوجد ملخص باللغة العربية
In a recent issue of Linguistics and Philosophy Kasmi and Pelletier (1998) (K&P), and Westerstahl (1998) criticize Zadroznys (1994) argument that any semantics can be represented compositionally. The argument is based upon Zadroznys theorem that every meaning function m can be encoded by a function mu such that (i) for any expression E of a specified language L, m(E) can be recovered from mu(E), and (ii) mu is a homomorphism from the syntactic structures of L to interpretations of L. In both cases, the primary motivation for the objections brought against Zadroznys argument is the view that his encoding of the original meaning function does not properly reflect the synonymy relations posited for the language. In this paper, we argue that these technical criticisms do not go through. In particular, we prove that mu properly encodes synonymy relations, i.e. if two expressions are synonymous, then their compositional meanings are identical. This corrects some misconceptions about the function mu, e.g. Janssen (1997). We suggest that the reason that semanticists have been anxious to preserve compositionality as a significant constraint on semantic theory is that it has been mistakenly regarded as a condition that must be satisfied by any theory that sustains a systematic connection between the meaning of an expression and the meanings of its parts. Recent developments in formal and computational semantics show that systematic theories of meanings need not be compositional.
We exhibit that the implicit UCCA parser does not address numeric fused-heads (NFHs) consistently, which could result either from inconsistent annotation, insufficient training data or a modelling limitation. and show which factors are involved. We c
Synonymy and translational equivalence are the relations of sameness of meaning within and across languages. As the principal relations in wordnets and multi-wordnets, they are vital to computational lexical semantics, yet the field suffers from the
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training data
How meaning is represented in the brain is still one of the big open questions in neuroscience. Does a word (e.g., bird) always have the same representation, or does the task under which the word is processed alter its representation (answering can y
In this work, we give rigorous operational meaning to superposition of causal orders. This fits within a recent effort to understand how the standard operational perspective on quantum theory could be extended to include indefinite causality. The mai