840 research outputs found
Semantic Ambiguity and Perceived Ambiguity
I explore some of the issues that arise when trying to establish a connection
between the underspecification hypothesis pursued in the NLP literature and
work on ambiguity in semantics and in the psychological literature. A theory of
underspecification is developed `from the first principles', i.e., starting
from a definition of what it means for a sentence to be semantically ambiguous
and from what we know about the way humans deal with ambiguity. An
underspecified language is specified as the translation language of a grammar
covering sentences that display three classes of semantic ambiguity: lexical
ambiguity, scopal ambiguity, and referential ambiguity. The expressions of this
language denote sets of senses. A formalization of defeasible reasoning with
underspecified representations is presented, based on Default Logic. Some
issues to be confronted by such a formalization are discussed.Comment: Latex, 47 pages. Uses tree-dvips.sty, lingmacros.sty, fullname.st
Language, logic and ontology: uncovering the structure of commonsense knowledge
The purpose of this paper is twofold: (i) we argue that the structure of commonsense knowledge must be discovered, rather than invented; and (ii) we argue that natural
language, which is the best known theory of our (shared) commonsense knowledge, should itself be used as a guide to discovering the structure of commonsense knowledge. In addition to suggesting a systematic method to the discovery of the structure of commonsense knowledge, the method we propose seems to also provide an explanation for a number of phenomena in natural language, such as metaphor, intensionality, and the semantics of nominal compounds. Admittedly, our ultimate goal is quite ambitious, and it is no less than the systematic ‘discovery’ of a well-typed
ontology of commonsense knowledge, and the subsequent formulation of the longawaited goal of a meaning algebra
Stochastic LLMs do not Understand Language: Towards Symbolic, Explainable and Ontologically Based LLMs
In our opinion the exuberance surrounding the relative success of data-driven
large language models (LLMs) is slightly misguided and for several reasons (i)
LLMs cannot be relied upon for factual information since for LLMs all ingested
text (factual or non-factual) was created equal; (ii) due to their subsymbolic
na-ture, whatever 'knowledge' these models acquire about language will always
be buried in billions of microfeatures (weights), none of which is meaningful
on its own; and (iii) LLMs will often fail to make the correct inferences in
several linguistic contexts (e.g., nominal compounds, copredication, quantifier
scope ambi-guities, intensional contexts. Since we believe the relative success
of data-driven large language models (LLMs) is not a reflection on the symbolic
vs. subsymbol-ic debate but a reflection on applying the successful strategy of
a bottom-up reverse engineering of language at scale, we suggest in this paper
applying the effective bottom-up strategy in a symbolic setting resulting in
symbolic, explainable, and ontologically grounded language models.Comment: 17 page
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