Semantic Structure and Logical Form

In this paper I examine a cluster of concepts relevant to the methodology of truth theories: 'informative definition', 'recursive method', 'semantic structure', 'logical form', 'compositionality', etc. The interrelations between these concepts, I will try to show, are more intricate and multi-dimensional than commonly assumed.

Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive.Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.

The validity of some inferences is said to be explained by reference to the meanings of the particular expressions occurring in them, while that of other inferences is due, rather, to the way in which the sentences are constructed out of their parts. The inference from ‘John knows that snow is white’ to ‘Snow is white’ is given as an example of the first type of inference; for it is said to be explained by providing an analysis of the semantical primitive ‘knows’. The inferences from ‘John ran breathlessly’ to ‘John ran’ and from ‘John is a large man’ to ‘John is a man’ may, tentatively, be taken to be examples of the second type.

The key note of the following remarks will be the distinction between two kinds of attitudes towards the relations between syntax and semantics, which I shall call without any claim to historical accuracy the Fregean and the Russellian attitudes, respectively. My main point will be that as far as the formal analysis of natural languages is concerned, the trend in recent years has been contrary to what Hintikka would have us believe towards more Fregean approaches. By naming them thus I by no means want to imply that these two important precursors of contemporary research in the philosophy of language and linguistic semantics would agree with most of what I will attribute to them. The distinction I want to draw does, however, cut across many of the issues dealt with in Hintikka's paper; to some of these I will turn shortly. But I would first like to motivate the distinction and to argue briefly for one of the two. The Fregean and Russellian attitudes are essentially alternatives to ways of considering the relation between syntax and semantics within one's theory of language. The Fregean attitude consists in regarding the relation between entities from the two domains as roughly one-to-one. What it basically entails is that the syntactic decomposition of the expressions of the language must enter directly into the semantic computation. A Fregean analysis is typically surface oriented and forsees in general for each syntactic category of the language (or rather of the syntax) a corresponding semantic category. On the other hand, what I am calling the Russellian attitude towards the relation between syntax and semantics is based on the view that the direct surface structure of a language (here I am of course referring to natural languages) is not a faithful guide to its semantic structure, or if you want, to its logical form. Typically, a Russellian attitude will lead to a kind of "syntactic transformationalism", best known, for example, from the kind of work done within the various trends of generative trans formational grammar (where the motivation for transformational analyses was of course not explicitly semantic). One could cite among

Formal pattern languages are used in industry to communicate and analyse requirements, as they are said to be both machine-readable and intuitively understandable for humans. The questions arise to what extent this intuitive understanding of a pattern language is in agreement with its formal semantics and whether this understanding can be increased systematically. We present two consecutive empirical experiments to address these questions. The formal semantics serves as an objective judge on the intuitive understanding. Our experiments confirm the practical usefulness of HanforPL insofar the intuition matches the formal semantics in most practically relevant cases. They also reveal a number of edge cases where even a prior exposure to formal logic is not a guarantee for correct understanding. We present and validate systematic adjustments to the patterns, leading to several large increases in understandability but come at the cost of new, but less impactful ambiguities. We demonstrate how an inquiry on the alignment of the intuitive and formal semantics of a pattern language can help to understand and improve the language. While results regarding the understandability of HanforPL are favourable in commonly used cases, there is potential for improvement. The systematic adaption of patterns shows that small modifications may have large effects on the alignment of formal and intuitive semantics, and that modification must be considered with caution in the context of the respective pattern to avoid unintentionally adding new ambiguities. This article is an extension of our published REFSQ paper.

An E-Librarian Service allows users to enter complete questions in natural language (NL) in order to improve the retrieval capabilities (see section 1.3.3). The aim of the natural language processing (NLP) module of an E-Librarian Service is to translate a user question into a logical and computer-readable form. The more precise and accurate this translation is, the more complex the algorithm gets. In this chapter we present the necessary principles to design the NLP module of an E-Librarian Service that is able to “understand” the meaning of a user question and to translate it into a logical form.

Clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.

It has been one of the strange and unexamined features of contemporary philosophy that philosophical as well as technical concern with the formal sciences — formal logic and mathematics, formal syntax and semantics — has been taken to be an ally of a positivistic, formalistic and (narrowly) analytic philosophical perspective. I believe that upon closer questioning, this alleged alliance breaks down, and the prima facie neutrality of the concern with formalism leaves room for a great diversity of philosophical standpoints — from a Platonic to a Kantian, to name two familiar contrasting positions. It is also a philosophically unexamined point of view, which requires that an understanding of the nature of the formal sciences must itself be formalistic, that you truely understand the nature of a formal discipline when you embed it in a meta — discipline of the same nature. There is certainly a sense of “understanding” in which this is the case. But there is a sense of “understanding”, a sense that is of central importance to philosophy — in which one can ask questions about the formal sciences which do not permit any non-trivially formalised answer.

The article provides an in-depth analysis of argumentation, which has been a foundational method in scientific discourse since ancient times and has evolved into a key component of interdisciplinary research. This evolution has positioned argumentation as an integral aspect of modern methodology of economic science. It was established that argumentation is a purposeful activity aimed at either reinforcing or undermining the convictions of others, involving a dialogue between participants to achieve specific objectives. The extensive development of argumentation as a scientific method has given rise to various approaches, types, methods, models, and implementation rules, thus emphasizing its complexity. Depending on the method of obtaining information, argumentation is commonly categorized into empirical and theoretical types. Logical (or rational) and pragmatic (or rhetorical, emotional) argumentation are distinguished by their implementation methods. Furthermore, argumentation can be differentiated based on its construction method, including causal, direct, and indirect argumentation. Additionally, argumentation can be classified by structure into simple (single) and complex (involving several arguments). The fundamental principles for constructing the structural components of argumentation include thesis rules, argument rules, and demonstration rules. Deviation from these rules leads to the use of the opposing method of argumentation – refutation. The contemporary stage of argumentation development integrates formal and informal logic techniques resulting from the historical evolution of philosophical and economic thought. In the modern theory of argumentation, formal logic provides a precise set of rules regarding the structural elements of argumentation, which are essential for its validation. Informal logic views argumentation as a form of dialogue involving natural language and practical examples. The success of the argumentation is evaluated using "good-bad" concepts, with credibility, acceptability, and probability being key factors.

Semantic information is important for precise word sense disambiguation system and the kind of semantic analysis used in sophisticated natural language processing such as machine translation, question answering, etc.There are at least two kinds of semantic information: lexical semantics for words and phrases and structural semantics for phrases and sentences.We have built a Japanese corpus of over three million words with both lexical and structural semantic information.In this paper, we focus on our method of annotating the lexical semantics, that is building a word sense tagged corpus and its properties.

This paper does not deal with the topic of ‘the generosity of artificial languages from an Asian or a comparative perspective’. Rather, it is concerned with a particular case taken from a development in the Western tradition, when in the wake of the rise of formal logic at the end of the nineteenth and the beginning of the twentieth century people in philosophy and later in linguistics started to use formal languages in the study of the semantics of natural languages. This undertaking rests on certain philosophical assumptions and instantiates a particular methodology, that we want to examine critically. However, that in itself is still too broad a topic for a single paper, so we will focus on a particular aspect, viz., the distinction between grammatical form and logical form and the crucial role it plays in how the relationship between natural languages and formal languages is understood in this tradition. We will uncover two basic assumptions that underlie the standard view on the distinction between grammatical form and logical form, and discuss how they have contributed to the shaping of a particular methodology and a particular view on the status of semantics as a discipline. But before we turn to the topic at hand, a few more words on the general nature of the investigation are in order. Its general aim is to find out how semantics constructs its object, i.e., we are interested in what semanticists view as the proper object of study, how they think this object can best be approached, and how they view the relations between their own undertaking and neighbouring disciplines that deal with related, or even the same kind of phenomena, such as cognitive psychology, cognitive neuroscience, philosophy, anthropology. The background assumption is that, much like in other disciplines, semantics, too, does not have its object of investigation cut out for itself by nature, but constructs it in a complex process that involves empirical elements (‘facts’ being already too dangerous a term), philosophical assumptions, and borrowings

Formal Logic and Formal Semantics #1

This paper attempts to survey recent issues in Logic, Language and Computation, and also serves as an introduction to the present book. Of course, my survey is not exhaustive, but it will provide background information to the reader for understanding current topics in these areas. In particular, I give an exposition of recent topics in formal logic, formal semantics and artificial intelligence (AI). I also try to discuss some important directions for future work in connection with several contributions in this book.KeywordsModal LogicLogic ProgrammingIntuitionistic LogicParaconsistent LogicNonmonotonic ReasoningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As service design has gained interest in the last years, so has gained one of its primary tools: the Service Blueprint. In essence, a service blueprint is a graphical tool for the design of business models, specifically for the design of business service operations. Despite its level of adoption, tool support for service design tasks is still on its early days and available tools for service blueprint modeling are mainly focused on enhancing usability and enabling collaborative edition, disregarding the formal aspects of modeling. In this paper we present a way to support the validation of service blueprint models by simulation. This approach is based on annotating the models with formal semantics, so that each task can be translated into formal logics, and from them, to executable SQL statements. This works opens a new direction in the way to bridge formal techniques and creative service design processes.

[Context and motivation] Formal pattern languages with a restricted English grammar, such as the pattern language of Konrad and Cheng, give us the possibility to combine human intuition and the rigour of a machine. [Question/problem] The question arises to what extent the intuitive understanding of such a pattern language is in agreement with its formal semantics. [Principal ideas/results] We present an empirical study to address this question. The existence of a formal semantics allows us to use the machine as an objective judge to decide if the intuitive understanding is correct. The study confirms empirically the practical usefulness of HanforPL in that the intuitive understanding matches the formal semantics in most practically relevant cases. The study reveals that a number of phrases of interest represent critical edge cases where even a prior exposure to formal logic is not a guarantee for the correct intuitive understanding. [Contribution] We show how the alignment of formal and intuitive semantics can be investigated, and that this alignment can not simply be assumed. Nonetheless, results regarding the understandability of HanforPL are favourable with high understandability in commonly used patterns. The results of the study will be the basis of improvements in HanforPL.