Logical Semantics and Commonsense Knowledge: Where Did we Go Wrong, and
How to Go Forward, Again

We argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts: ontological concepts, that should be types in a strongly-typed ontology, and logical concepts, that are predicates corresponding to properties of and relations between objects of various ontological types. We will then show that accounting for these differences amounts to the integration of lexical and compositional semantics in one coherent framework, and to an embedding in our logical semantics of a strongly-typed ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. We will show that in such a framework a number of challenges in natural language semantics can be adequately and systematically treated.
https://arxiv.org/abs/1808.01741
9 Aug 2018

On the Winograd Schema: Situating Language Understanding in the Data-Information-Knowledge Continuum


The Winograd Schema (WS) challenge, proposed as an alternative to the Turing Test, has become the new standard for evaluating progress in natural language understanding (NLU). In this paper we will not however be concerned with how this challenge might be addressed. Instead, our aim here is threefold: (i) we will first formally ‘situate’ the WS challenge in the data-information-knowledge continuum, suggesting where in that continuum a good WS resides; (ii) we will show that a WS is just a special case of a more general phenomenon in language understanding, namely the missing text phenomenon (henceforth, MTP) - in particular, we will argue that what we usually call thinking in the process of language understanding involves discovering a significant amount of ‘missing text’ - text that is not explicitly stated, but is often implicitly assumed as shared background knowledge; and (iii) we conclude with a brief discussion on why MTP is inconsistent with the data-driven and machine learning approach to language understanding.
https://arxiv.org/abs/1810.00324

Feb 5 2019

Back to the Drawing Board: The Myth of Data-Driven NLU and the Roadmap going Forward

A talk given at SRI International

Here

More Papers on DBLP

More Papers on Google Scholar