Negation

2006 ◽  
Vol 72 (1) ◽  
pp. 233-252 ◽  
Author(s):  
Ernesto Napoli
Keyword(s):  
Natural Language ◽  
Ambiguous Word ◽  
Natural Languages ◽  
Artificial Languages ◽  
Negation Sign

The paper is concerned with negation in artificial and natural languages. "Negation" is an ambiguous word. It can mean three different things: An operation(negating), an operator (a sign of negation), the result of an operation. The threethings, however, are intimately linked. An operation such as negation, is realizedthrough an operator of negation, i.e. consists in adding a symbol of negation to an entity to obtain an entity of the same type; and which operation it is dependson what it applies to and on what results from its application. I argue that negation is not an operation on linguistic acts but rather anoperation on the objects of linguistic acts, namely sentences. And I assume that the negation of a sentence is a sentence that contradicts it. If so, the negation of a sentence may be obtained, in case the sentence is molecular, by applying the operation of negation not to the sentence itself but to a constituent sentence. To put it in a succinct and paradoxically sounding way we could say that in order to negate a sentence it is sufficient but not necessary to negate it. However that negation applies to sentences is true only for artificial languages, in which the sign of negation is a monadic sentential connective. In natural language, negation applies to expressions other than sentences, namely word sand non-sentential phrases. Still words and not sentential phrases are interesting and valuable only as ultimate or immediate constituents of sentences, as a means of saying (something that can be true or false) and the concern with negation is ultimately the concern with the negation of sentences. So the problem is what sub-sentential and non sentential expressions negation should apply to in order to obtain the negation of the containing sentence. The standard answer is that the negation of a natural language sentence is equivalent to the negation of its predicate. Yet, I argue, predicate negation is necessary but not sufficient, due to the existence of molecular sentences. Finally I notice that if to apply negation to an artificial sentence is to put the negation sign in front of it, to negate the predicate of a natural language sentencemay or may not be to put the negation sign in front of it.

The Liberal Arts and the End of Education

1998 ◽  
pp. 137-141
Author(s):  
Kathleen Haney
Keyword(s):  
Natural Language ◽  
Liberal Education ◽  
Liberal Arts ◽  
Mother Tongue ◽  
Undergraduate Curriculum ◽  
Natural Languages ◽  
International Conference ◽  
Artificial Languages ◽  
The Arts ◽  
Historical Tradition

An international conference that takes Philosophy Educating Humanity as its theme does well to revisit the liberal arts tradition. Although the liberal arts are most often assimilated to studies brought together as the Humanities, the old usage included the arts which employed artificial languages in mathematics, music, and astronomy, as well as the literature and letters of the various natural languages. The current conflation of liberal education with the humanities does violence to the historical tradition in education, reducing it to fluff in the eyes of tough-minded scientists who know that only numbers deliver objectivity. The liberal arts of the traditional undergraduate curriculum provided the skills to liberate the student's linguistic powers so that he or she could read, speak, and understand natural language in all its functions. To educate human persons to master language is to encourage students to take possession of their natural powers so that they can express themselves, understand what others say, and reason together. The arts of natural language lead to mastery of the mathematical arts which use a language that is no one's mother tongue. Together, the seven arts rid students of the worst enemies of humankind: ignorance and prejudice.

The translator’s sensitivity to syntactic ambiguity—a psycholinguistics experiment

1976 ◽  
Vol 21 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Elyse Piquette
Keyword(s):  
Natural Language ◽  
Finite Number ◽  
Infinite Number ◽  
Syntactic Ambiguity ◽  
Natural Languages ◽  
Transformational Grammar ◽  
Artificial Languages ◽  
Language Structure ◽  
Linguistic Representations ◽  
Important Notion

Transformational grammar has attempted to outline the systematic nature of language structure while also stressing the creative aspect of language. Language is systematic in that speakers use a finite number of means to make up their messages, and yet it is creative in that there are an infinite number of individual different messages which are possible in any natural language. In natural languages, however, there is not a perfect one-to-one correspondence between possible messages—intended or perceived—and possible linguistic realizations, as there exists in conventional or artificial languages. Often it is found in natural languages that a single linguistic form may have two or more meanings. Homonymy, whether it is lexical or syntactic, is an important notion, not only because syntactic ambiguity plays a central role in linguistic theory, but also because its study gives us a better understanding of the systematics of language and of the way we attach meaning to linguistic representations. Hence, the importance of evaluating how speakers deal with syntactic ambiguity in their attempts to understand and to be understood.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 109-117
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Logic ◽  
Natural Language ◽  
Semantic Interpretation ◽  
General View ◽  
World Model ◽  
Semantic Approach ◽  
Natural Languages ◽  
The World ◽  
Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

Principles of compiling artificial languages

2020 ◽  
Vol 56 (07) ◽  
pp. 40-46
Author(s):  
Khayala Mugamat Mursaliyeva ◽  
Keyword(s):  
Natural Language ◽  
Modern World ◽  
Linguistic Landscape ◽  
Artificial Language ◽  
Natural Consequence ◽  
Modern Language ◽  
Artificial Languages ◽  
The World ◽  
The Creation

The explosion of information and the ever-increasing number of international languages make the modern language situation very difficult. The interaction of languages ultimately leads to the creation of international artificial languages that operate in parallel with the world`s languages. The expansion of interlinguistic issues is a natural consequence of the aggravation of the linguistic landscape of the modern world. The modern interlinguistic dialect, which is defined as a field of linguistics that studies international languages and international languages as a means of communication, deals with the importance of overcoming the barrier.The problem of international artificial languages is widely covered in the writings of I.A.Baudouin de Courtenay, V.P.Qrigorev, N.L.Gudskov, E.K.Drezen, A.D.Dulchenko, M.I.Isayev, S.N.Kuznechov, A.D.Melnikov and many other scientists. Key words:the concept of natural language, the concept of artificial language, the degree of artificiality of language, the authenticity of language

A UNIFIED AND SYNCHRONOUS GENERATING SYSTEM FOR MULTIPLE NATURAL LANGUAGES BASED ON CFG AND SEMANTIC LANGUAGE

2012 ◽  
Vol 10 (04) ◽  
pp. 1250033
Author(s):  
LI LI ◽  
HONGLAI LIU ◽  
QINGSHI GAO ◽  
PEIFENG WANG
Keyword(s):  
Natural Language ◽  
Natural Languages ◽  
Generating System

The sentences in several different natural languages can be produced congruously and synchronous by the new generating system USGS = {↔, GI|GI = (TI, N, B-RISU, C-treeI, S, PI, FI), I = 0, 1, 2, …, n}, based on Semantic Language(SL) theory, all are legitimate and reasonable, where, B-RISU is the set of basic-RISU, C-treeI is the set of category-trees, and FI is the set of functions in I-natural language. The characteristic of this new generating system is unified, synchronous and one by one corresponding, based on semantic unit theory and that the number of rules is several millions.

scholarly journals Natural Language Processing by Enhanced Honey Encryption Technique

2019 ◽  
Vol 8 (12S) ◽  
pp. 159-163
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Cyber Attacks ◽  
Binary Form ◽  
Brute Force ◽  
Natural Languages ◽  
Cipher Text ◽  
The Right ◽  
Binary Strings

Traditional encryption systems and techniques have always been vulnerable to brute force cyber-attacks. This is due to bytes encoding of characters utf8 also known as ASCII characters. Therefore, an opponent who intercepts a cipher text and attempts to decrypt the signal by applying brute force with a faulty pass key can detect some of the decrypted signals by employing a mixture of symbols that are not uniformly dispersed and contain no meaningful significance. Honey encoding technique is suggested to curb this classical authentication weakness by developing cipher-texts that provide correct and evenly dispersed but untrue plaintexts after decryption with a false key. This technique is only suitable for passkeys and PINs. Its adjustment in order to promote the encoding of the texts of natural languages such as electronic mails, records generated by man, still remained an open-end drawback. Prevailing proposed schemes to expand the encryption of natural language messages schedule exposes fragments of the plaintext embedded with coded data, thus they are more prone to cipher text attacks. In this paper, amending honey encoded system is proposed to promote natural language message encryption. The main aim was to create a framework that would encrypt a signal fully in binary form. As an end result, most binary strings semantically generate the right texts to trick an opponent who tries to decipher an error key in the cipher text. The security of the suggested system is assessed..

scholarly journals When regularization gets it wrong: children over-simplify language input only in production

2018 ◽  
Vol 45 (5) ◽  
pp. 1054-1072 ◽  
Author(s):  
Jessica F. SCHWAB ◽  
Casey LEW-WILLIAMS ◽  
Adele E. GOLDBERG
Keyword(s):  
Complex System ◽  
High Accuracy ◽  
Forced Choice ◽  
Choice Task ◽  
The Other ◽  
Natural Languages ◽  
Multiple Factors ◽  
Artificial Languages ◽  
Forced Choice Task ◽  
Unpredictable Variation

AbstractChildren tend to regularize their productions when exposed to artificial languages, an advantageous response to unpredictable variation. But generalizations in natural languages are typically conditioned by factors that children ultimately learn. In two experiments, adult and six-year-old learners witnessed two novel classifiers, probabilistically conditioned by semantics. Whereas adults displayed high accuracy in their productions – applying the semantic criteria to familiar and novel items – children were oblivious to the semantic conditioning. Instead, children regularized their productions, over-relying on only one classifier. However, in a two-alternative forced-choice task, children's performance revealed greater respect for the system's complexity: they selected both classifiers equally, without bias toward one or the other, and displayed better accuracy on familiar items. Given that natural languages are conditioned by multiple factors that children successfully learn, we suggest that their tendency to simplify in production stems from retrieval difficulty when a complex system has not yet been fully learned.

How to build a constructicon in five years

2021 ◽  
Vol 34 ◽  
Author(s):  
Laura A. Janda ◽  
Anna Endresen ◽  
Valentina Zhukova ◽  
Daria Mordashova ◽  
Ekaterina Rakhilina
Keyword(s):  
Open Access ◽  
Natural Language ◽  
Language Proficiency ◽  
Construction Grammar ◽  
Full Scale ◽  
Electronic Database ◽  
Natural Languages ◽  
Searchable Database ◽  
Qualitative Level ◽  
Syntactic Properties

Abstract We provide a practical step-by-step methodology of how to build a full-scale constructicon resource for a natural language, sharing our experience from the nearly completed project of the Russian Constructicon, an open-access searchable database of over 2,200 Russian constructions (https://site.uit.no/russian-constructicon/). The constructions are organized in families, clusters, and networks based on their semantic and syntactic properties, illustrated with corpus examples, and tagged for the CEFR level of language proficiency. The resource is designed for both researchers and L2 learners of Russian and offers the largest electronic database of constructions built for any language. We explain what makes the Russian Constructicon different from other constructicons, report on the major stages of our work, and share the methods used to systematically expand the inventory of constructions. Our objective is to encourage colleagues to build constructicon resources for additional natural languages, thus taking Construction Grammar to a new quantitative and qualitative level, facilitating cross-linguistic comparison.

Discovering Opinions from Customers' Unstructured Textual Reviews Written in Different Natural Languages

2014 ◽  
pp. 834-859
Author(s):  
Jan Žižka ◽  
František Dařena
Keyword(s):  
Decision Tree ◽  
Natural Language ◽  
Real World ◽  
Large Data ◽  
Main Question ◽  
Internet Shopping ◽  
Natural Languages ◽  
On Line ◽  
Multilingual Data ◽  
Textual Form

Gaining new and keeping existing clients or customers can be well-supported by creating and monitoring feedbacks: “Are the customers satisfied? Can we improve our services?” One of possible feedbacks is allowing the customers to freely write their reviews using a simple textual form. The more reviews that are available, the better knowledge can be acquired and applied to improving the service. However, very large data generated by collecting the reviews has to be processed automatically as humans usually cannot manage it within an acceptable time. The main question is “Can a computer reveal an opinion core hidden in text reviews?” It is a challenging task because the text is written in a natural language. This chapter presents a method based on the automatic extraction of expressions that are significant for specifying a review attitude to a given topic. The significant expressions are composed using significant words revealed in the documents. The significant words are selected by a decision-tree generator based on the entropy minimization. Words included in branches represent kernels of the significant expressions. The full expressions are composed of the significant words and words surrounding them in the original documents. The results are here demonstrated using large real-world multilingual data representing customers' opinions concerning hotel accommodation booked on-line, and Internet shopping. Knowledge discovered in the reviews may subsequently serve for various marketing tasks.

Sign in / Sign up

Export Citation Format

Share Document