Walid Saba

The purpose of this paper is twofold: (i) we will argue that formal semantics
might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts, namely 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; and (ii) we show that accounting for these differences amounts to
a new… Show more

The purpose of this paper is twofold: (i) we will argue that formal semantics
might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts, namely 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; and (ii) we show that accounting for these differences amounts to
a new formal semantics; one that integrates lexical and compositional semantics
in one coherent framework and one where formal semantics is embedded with a
strongly typed ontology; an ontology that reflects our commonsense knowledge
of the world and the way we talk about it in ordinary language. We will show
how in such a framework a number of challenges in the semantics of natural
language are adequately and systematically treated. Show less

The Winograd Schema (WS) challenge, proposed as an al-ternative 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… Show more

The Winograd Schema (WS) challenge, proposed as an al-ternative 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 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 by a brief discussion on why MTP is inconsistent with the data-driven and machine learning approach to language understanding. Show less

The purpose of this paper is twofold: (i) we will argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts, namely 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; and (ii) we will show that accounting for these differences amounts to the integration of… Show more

The purpose of this paper is twofold: (i) we will argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts, namely 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; and (ii) we will 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, an ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. In such a framework, as will be demonstrated, a number of challenges in the semantics of natural language can be ade-quately and systematically treated. Show less

Over two decades ago, a 'quiet revolution' overwhelmingly replaced knowledge-based approaches in natural language processing (NLP) by quantitative (e.g., statistical, corpus-based, machine learning) methods. Although it is our firm belief that purely quantitative approaches cannot be the only paradigm for NLP, dissatisfaction with purely engineering approaches to the construction of large knowledge bases for NLP are somewhat justified. In this paper we hope to demonstrate that both trends are… Show more

Over two decades ago, a 'quiet revolution' overwhelmingly replaced knowledge-based approaches in natural language processing (NLP) by quantitative (e.g., statistical, corpus-based, machine learning) methods. Although it is our firm belief that purely quantitative approaches cannot be the only paradigm for NLP, dissatisfaction with purely engineering approaches to the construction of large knowledge bases for NLP are somewhat justified. In this paper we hope to demonstrate that both trends are partly misguided and that the time has come to enrich logical semantics with an ontological structure that reflects our commonsense view of the world and the way we talk about in ordinary language. Show less

We suggest modelling concepts as types in a strongly-typed ontology that reflects our commonsense view of the world and the way we talk about in ordinary spoken language. In such a framework, certain types of ambiguities in natural language are explained by the notion of polymoprhism.

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… Show more

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 long-awaited goal of a meaning algebra. Show less

Despite overwhelming evidence suggesting that quantifier scope is a phenomenon that must be treated at the pragmatic level, most computational treatments of scope ambiguities have thus far been a collection of syntactically motivated preference rules. This might be in part due to the prevailing wisdom that a commonsense inferencing strategy would require the storage of and reasoning with a vast amount of background knowledge. In this paper we hope to demonstrate that the challenge in developing… Show more

Despite overwhelming evidence suggesting that quantifier scope is a phenomenon that must be treated at the pragmatic level, most computational treatments of scope ambiguities have thus far been a collection of syntactically motivated preference rules. This might be in part due to the prevailing wisdom that a commonsense inferencing strategy would require the storage of and reasoning with a vast amount of background knowledge. In this paper we hope to demonstrate that the challenge in developing a commonsense inferencing strategy is in the discovery of the relevant commonsense data and in a proper formulation of the inferencing strategy itself, and that a massive amount of background knowledge is not always required. In particular, we present a very effective procedure for resolving quantifier scope ambiguities at the pragmatic level using simple ‘quantitative’ data that is readily available in most database environments. Show less