Overcoming the fear of semantics

This paper discusses how to design robot-human communication using Ontological Semantic Technology (OST), which is to address meanings of phrases or sentences in natural languages, in a firefighting domain. The OST is a system in an ontology-based structure to deal with different natural languages. In this study, English and Korean were selected to be implemented for the OST. The problem set is designed with Natural English, direct translation from the natural English to Korean, natural Korean, and direct translation from the natural Korean to English, which can be compared to examine the similarity of meanings with a language-independent ontology in processing English and Korean.

In this paper, we present a system that translates first order logic (FOL) formulas into natural language (NL) sentences. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where it is used as a means for feedback to the users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching capabilities of rules. So, the system consists of a rule-based component and a lexicon. The rule-based unit implements the conversion process, which is based on a linguistic analysis of a FOL sentence, and the lexicon provides lexical and grammatical information that helps in producing the NL sentences. The whole system is implemented in Jess, a java-based expert system shell. The conversion process currently covers a restricted set of FOL formulas.

This article discusses what kind of entity the proposed semantic Web is, principally by reference to the relationship of natural language structure to knowledge representation. The concept of the SW has two distinct origins, and this, bifurcation persists in two differing lines of SW research: one closely allied to notions of documents and natural language processing (NLP) and one not. These differences of emphasis or content carry with them different commitments about what it is to interpret a KR and what the interpretation method has to do with meaning in natural language.

The year is 2030. A two-young-child, two-parent household, the Rubensteins, owns and employs a state-of-the-art household robot, Rodney. With the parents out, the children ask Rodney to perform some action \(\alpha \) that violates a Rubensteinian ethical principle \(P_R\). Rodney replies: (\(s_1\)) “Doing that would be (morally) wrong, kids.” The argument the children give Rodney in protest is that another household, the Müllers, also has a robot, Ralph; and the kids argue that he routinely performs \(\alpha \). As a matter of fact, Ralph’s doing \(\alpha \) violates no Müllerian ethical principle \(P_M\). Ralph’s response to the very same request from the children he tends is: (\(s_2\)) “Okay, doing that is (morally) fine, kids.” What is the meaning of the utterances made by Rodney and Ralph? We answer this question by presenting and employing a novel, formal, inferential theory of meaning in natural language: hypergraphical inferential semantics (\(\mathscr {H}\!\! \mathscr {I}\!\! \mathscr {S}\)), which is in the general spirit of proof-theoretic semantics, which is in turn antithetical to Montagovian model-theoretic semantics. \(\mathscr {H}\!\! \mathscr {I}\!\! \mathscr {S}\), applied even to sentences logically simpler than \(s_1\) and \(s_2\), implies that human-level natural language understanding (NLU) is Turing-uncomputable.

Frame semantics is a theory of meaning in natural language, which defines the structure of the lexical semantic resources known as framenets. Both framenets and frame semantics have proved useful for a number of natural language processing (NLP) tasks. However, in this connection framenets have often been criticized for their limited coverage. A proposed reasonable-effort solution to this problem is to develop domain-specific (sublanguage) framenets to complement the corresponding general-language framenets for particular NLP tasks, and in the literature we find such initiatives covering domains such as medicine, soccer, and tourism. In this paper, we report on building a framenet to cover the terms and concepts encountered in descriptive linguistic grammars (written in English) i.e. a framenet for the linguistic domain (LingFN) to complement the general-language BFN.KeywordsFrame semanticsFramenetsSemantics

There has been extensive debate about the principle of compositionality among philosophers of language and formal semanticists, according to which the meaning of a complex expression is determined by the meanings of its parts and the way these parts are combined. Questions about this principle can be broadly divided into three categories: why we want compositionality, what the proper definition of compositionality should be, and how various semantic phenomena can be analyzed compositionally. In this paper, I will focus on the first question. After briefly surveying well-known arguments for compositionality, I will present two methodological arguments and examine their validity.

The paper presents a formal framework to model the fusion of gesture meaning with the meaning of the co-occurring verbal fragment. The framework is based on the formalization of two simple concepts, intersectivity and iconicity, which form the core of most descriptive accounts of the interaction of the two modalities. The formalization is presented as an extension of a well-known framework for the analysis of meaning in natural language. We claim that a proper formalization of these two concepts is sufficient to provide a principled explanation of gestures accompanying different types of linguistic expressions. The formalization also aims at providing a general mechanism (iconicity) by which the meaning of gestures is extracted from their formal appearance.

Chapter 2 - Categorial Type Logics

The project of giving an account of meaning in natural languages goes largely by assigning truth-conditional content to sentences. I will call the view that sentences have truth-conditional content propositionalism as it is common to identify the truth-conditional content of a sentence with the proposition it expresses. This content plays an important role in our explanations of the speech-acts, attitude ascriptions, and the meaning of sentences when they appear as parts of longer sentences. Much work in philosophy of language and linguistics semantics over the last halfcentury has aimed to characterize the truth-conditional content of different aspects of language.

Distributional semantics provides multidimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown by a large body of research in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are relevant for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar–semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The goal of this review is to foster greater cross-fertilization of theoretical and computational approaches to language as a means to advance our collective knowledge of how it works.

Logic as a knowledge representation and reasoning language is a fundamental topic of an Artificial Intelligence (AI) course and includes a number of sub-topics. One of them, which brings difficulties to students to deal with, is converting natural language (NL) sentences into first-order logic (FOL) formulas. To assist students to overcome those difficulties, we developed the NLtoFOL system and equipped it with a strong assistance and feedback mechanism. In this work, first, we present that feedback mechanism. The mechanism can provide assistance before an answer is submitted, if requested, but mainly it provides assistance after an answer is submitted. To that end, it characterizes the answer in terms of completeness and accuracy to determine the level of incorrectness, based on an answer categorization scheme, introduced in this paper. The automatically generated natural language feedback sequences grow from general to specific and can include statements on a student’s metacognitive state. Feedback is provided as natural language sentences automatically generated through a template-based natural language generation mechanism. Second, we present an extensive evaluation of the effectiveness of the assistance and feedback mechanism on students’ learning. The evaluation of feedback with students showed that full feedback sequences lead to greater learning gains than sequences consisting of only flag feedback and bottom-out hints (n = 226), and that generic, template-based feedback sequences are comparable to the utility of problem-specific hints generated by human tutors (n = 120).

Vision-Language Models (VLMs) have shown great success as foundational models for downstream vision and natural language applications in a variety of domains. However, these models are limited to reasoning over objects and actions currently visible on the image plane. We present a spatial extension to the VLM, which leverages spatially-localized egocentric video demonstrations to augment VLMs in two ways - through understanding spatial task-affordances, i.e. where an agent must be for the task to physically take place, and the localization of that task relative to the egocentric viewer. We show our approach outperforms the baseline of using a VLM to map similarity of a task's description over a set of location-tagged images. Our approach has less error both on predicting where a task may take place and on predicting what tasks are likely to happen at the current location. The resulting representation will enable robots to use egocentric sensing to navigate to, or around, physical regions of interest for novel tasks specified in natural language.

First-order logic augments the expressive power of propositional logic as it links the logical assertions to properties of objects of some non-empty universe: the domain of discourse. This is achieved by allowing the propositional symbols to take arguments that range over elements of the domain of discourse. These are now called predicate symbols and are interpreted as relations on the domain. Elements of the domain of discourse are denoted by terms built up from variables, constants, and functions applied to other terms. First-order logic also expands the lexicon of propositional logic with the quantifiers “for all” and “there exists” that are interpreted consistently with their natural language meaning.This chapter is devoted to classical first-order logic. Our presentation is similar to the one conducted for propositional logic. We first define the syntax of first-order logic, followed by its semantics. Next we define a proof system for it and present the fundamental theoretical results of soundness and completeness. We also discuss the decision problems related to this logic. The remaining sections of the chapter cover variations and extensions of first-order logic, as well as first-order theories.

In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the technology in a restricted domain: COVID-19 policy design.

Testing the Validity of a Natural Spoken Language Application for the Self-Monitoring of Daily Dietary Intake (P13-035-19)