Comparative logic as an approach to comparison in natural language

Text is the backbone of the web and most of the information and human knowledge is represented in natural language. Every day, a vast amount of textual information is posted in web portals, wikis and news sites and necessitates automated approaches to analyze and understand their content. In this paper, we present a case-based reasoning approach to transform natural language sentences into first order logic formulas. The formalization approach relies on the principle that natural language sentences with similar grammatical structures and dependency trees would have similar representation in first order logic. The approach consists of two main stages. First, a deep analysis of the natural language sentence is performed, where proper characteristics are extracted and dependencies are specified. After that, in the second stage a cased based reasoning approach is used to utilize existing knowledge (formalized sentences) and drive the formalization of a new sentence. The similarity between natural language sentences is conducted on their dependency trees and is calculated based on the tree edit distance. Then, if needed, the adaptation of a solution is made based on rules. Example studies have shown the applicability of the method and the results on a small number of sentences are very promising.

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.

In natural language we meet sentences of quite different grammatical and logical structure. Sentential logic and the logic of quantifiers investigate some “basic structures” of language expressions. In the last few decades logicians have created a lot of formal systems to extend the expressive power and potential of formal languages in accordance with the multiplicity of forms in the natural language. In the main it has been by constructing ideal languages, i.e. under assumptions which are not fulfilled by natural language or by real language communication. Many problems arise if we try to apply logical analysis to philosophy or other sciences which use natural language.

Recognition Textual Entailment is a fundamental research direction in natural language processing. However, due to the complexity of sentence structure and the multiple parts of speech of words, it affects the model's deep understanding of sentences. From a linguistic perspective, different parts of speech have different understandings of sentences. We believe that a complete sentence consists of two parts: sentence structure and syntactic structure. In response to this issue, this article proposes a recognition textual entailment model that integrates attention mechanism and part of speech tagging. NLTK (Natural Language Toolkit) is used to annotate the part of speech of sentences, and the part of speech information is interactively fused with the sentence features extracted by the Conformer model. After part of speech tagging, the model can better grasp the syntactic structure of sentences. Experiments have shown that the above method combines sentence structure and syntactic structure, which can effectively enhance the model's understanding of deep semantics and improve its accuracy compared to classical models.

John Venn and logical theory

Natural language processing by computer

We explore an approach to merging simulation and natural language in which conceptual structures are used which can repre sent the structure and meaning of sentences as well as basic mathematical concepts. Sentences can be transformed to these structures, and, via language generation, the structures can be transformed into mathematical equations. The process is illustrated using a text description of a plankton respiration model. The ap proach shows how qualitative natural language statements can be merged with both qualitative and quantitative modeling and simulation.

Chapter 5 - GB Theory: An Introduction

Because of the hierarchical organization of natural languages, words that are syntactically related are not always linearly adjacent. For example, the subject and verb in the child always runs agree in person and number, although they are not adjacent in the sequences of words. Since such dependencies are indicative of abstract linguist structure, it is of significant theoretical interest how these relationships are acquired by language learners. Most experiments that investigate nonadjacent dependency (NAD) learning have used artificial languages in which the to-be-learned dependencies are isolated, by presenting the minimal sequences that contain the dependent elements. However, dependencies in natural language are not typically isolated in this way. We report the first demonstration to our knowledge of successful learning of embedded NADs, in which silences do not mark dependency boundaries. Subjects heard passages of English with a predictable structure, interspersed with passages of the artificial language. The English sentences were designed to induce boundaries in the artificial languages. In Experiment 1 & 3 the artificial NADs were contained within the induced boundaries and subjects learned them, whereas in Experiment 2 & 4, the NADs crossed the induced boundaries and subjects did not learn them. We take this as evidence that sentential structure was "carried over" from the English sentences and used to organize the artificial language. This approach provides several new insights into the basic mechanisms of NAD learning in particular and statistical learning in general. (PsycINFO Database Record

The application of Artificial Intelligence in Natural Language Processing (NLP) in today’s modern civilization is one that has attracted increasing attention in recent decades for quantitative analysing and depiction of human language. Linguistics is a discipline of language research that comprises acoustic phonology, morphological characteristics (word creation), syntax (sentence structure), semantics (grammar), and pragmatics (understanding). The paper identifies four dimensions by addressing various Natural Language Processing rates, accompanied by introducing the historical expansion; the current status as well as describing the most recent NLP implementations as well as upcoming trends and issues. The primary goal of the researcher would be to glean data on how humans acquire and interpret language and to assure that appropriate and effective tools and methods to be recognized which enables computer systems to perceive and manipulate natural languages in order to execute and complete essential functions. The paper will also demonstrate the role of Artificial Intelligence in operating Natural Language Processing, as well as the fundamental analysis typically associated to fulfil Natural Language Processing operational tasks embracing syntactic as well as semantic exploration. 

In this paper we present a new dimension to paraphrasing text in which characteristics of the original text motivate strategies for effective paraphrasing. Our system combines two existing robust components: the IRUS-II natural language understanding system and the SPOKESMAN generation system. We describe the architecture of the system and enhancements made to these components to facilitate paraphrasing. We particularly look at how levels of representation in these two systems are used by specialists in the paraphraser which define potential problems and paraphrasing strategies. Finally, we look at the role of paraphrasing in a cooperative dialog system. We will focus here on paraphrasing in the context of natural language interfaces and particularly on how multiple interpretations introduced by various kinds of ambiguity can be conbasted in paraphrases using both sentence structure and highlighting and formating the text itself.

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, “valency” is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling (SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

Purpose The purpose of this paper is to present the application of logical framework analysis (LFA) for implementing continuous quality improvement (CQI) across multiple settings in a tertiary care hospital. Design/methodology/approach This study adopts a multiple case study approach. LFA is implemented within three diverse settings, namely, intensive care unit, surgical ward, and acute in-patient psychiatric ward. First, problem trees are developed in order to determine the root causes of quality issues, specific to the three settings. Second, objective trees are formed suggesting solutions to the quality issues. Third, project plan template using logical framework (LOGFRAME) is created for each setting. Findings This study shows substantial improvement in quality across the three settings. LFA proved to be effective to analyse quality issues and suggest improvement measures objectively. Research limitations/implications This paper applies LFA in specific, albeit, diverse settings in one hospital. For validation purposes, it would be ideal to analyse in other settings within the same hospital, as well as in several hospitals. It also adopts a bottom-up approach when this can be triangulated with other sources of data. Practical implications LFA enables top management to obtain an integrated view of performance. It also provides a basis for further quantitative research on quality management through the identification of key performance indicators and facilitates the development of a business case for improvement. Originality/value LFA is a novel approach for the implementation of CQI programs. Although LFA has been used extensively for project development to source funds from development banks, its application in quality improvement within healthcare projects is scant.

Chapter 11 - Partiality

This article examines the role of gradable properties in referring expressions from the perspective of natural language generation. First, we propose a simple semantic analysis of vague descriptions (i.e., referring expressions that contain gradable adjectives) that reflects the context-dependent meaning of the adjectives in them. Second, we show how this type of analysis can inform algorithms for the generation of vague descriptions from numerical data. Third, we ask when such descriptions should be used. The article concludes with a discussion of salience and pointing, which are analyzed as if they were gradable adjectives.