Ambiguity and context learning in signalling games

2020 ◽  
Author(s):  
Liping Tang
Keyword(s):  
Natural Language ◽  
Learning Process ◽  
Adaptive Learning ◽  
Structural Evolution ◽  
Sufficient Condition ◽  
Natural Languages ◽  
Context Learning ◽  
Ambiguous Words ◽  
Lattice Network ◽  
Signalling Games

Abstract Lexical ambiguity is present in many natural languages, but ambiguous words and phrases do not seem to be advantageous. Therefore, the presence of ambiguous words in natural language warrants explanation. We justify the existence of ambiguity from the perspective of context dependence. The main contribution of the paper is that we constructed a context learning process such that each interlocutor can infer their opponent’s private belief from the conversation. A sufficient condition for successful learning is provided. Furthermore, for cases in which learning fails, we investigate how the interlocutors choose among degrees of ambiguous expressions through an adaptive learning process. Lastly, we apply our model in the lattice network, demonstrating that structural evolution favours ambiguity as well.

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).

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..

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.

Learning in multi-team systems: a qualitative study of learning triggers, readiness to learn and learning processes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valerie I. Sessa ◽  
Jessica L. Francavilla ◽  
Manuel London ◽  
Marlee Wanamaker
Keyword(s):  
Case Studies ◽  
Learning Process ◽  
Adaptive Learning ◽  
Quantitative Research ◽  
Participant Observation ◽  
Learning Processes ◽  
Future Research ◽  
Content Type ◽  
First Case ◽  
Readiness To Learn

Purpose Multi-team systems (MTSs) are expected to respond effectively to complex challenges while remaining responsive and adaptable and preserving inter-team linking mechanisms. The leadership team of an MTS is expected to configure and reconfigure component teams to meet the unique needs of each situation and perform. How do they learn to do this? This paper, using a recent MTS learning theory as a basis, aims to begin to understand how MTSs learn and stimulate ideas for future research. Design/methodology/approach The authors use two case studies to address research questions. The first case was a snapshot in time, while the second case occurred over several months. Interviews, documents and participant observation were the data sources. Findings As suggested by theory, findings support the idea that learning triggers, the timing of the triggers and readiness to learn (RtL) affect the type of learning process that emerges. The cases showed examples of adaptive and generative team learning. Strong and clear triggers, occurring during performance episodes, led to adaptive learning. When RtL was high and triggers occurred during hiatus periods, the associated learning process was generative. Originality/value Using an available theoretical model and case studies, the research describes how MTS readiness to learn and triggers for learning affect MTS learning processes and how learning outcomes became codified in the knowledge base or structure of the MTS. This provides a framework for subsequent qualitative and quantitative research.

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.

Syntax

2018 ◽  
Author(s):  
Stephen Neale
Keyword(s):  
Twentieth Century ◽  
Natural Language ◽  
Logical Form ◽  
Natural Languages ◽  
Related Notion ◽  
Theories Of Mind ◽  
Contemporary Work ◽  
Linguistic Categories ◽  
Traditional Work ◽  
Syntactic Properties

Syntax (more loosely, ‘grammar’) is the study of the properties of expressions that distinguish them as members of different linguistic categories, and ‘well-formedness’, that is, the ways in which expressions belonging to these categories may be combined to form larger units. Typical syntactic categories include noun, verb and sentence. Syntactic properties have played an important role not only in the study of ‘natural’ languages (such as English or Urdu) but also in the study of logic and computation. For example, in symbolic logic, classes of well-formed formulas are specified without mentioning what formulas (or their parts) mean, or whether they are true or false; similarly, the operations of a computer can be fruitfully specified using only syntactic properties, a fact that has a bearing on the viability of computational theories of mind. The study of the syntax of natural language has taken on significance for philosophy in the twentieth century, partly because of the suspicion, voiced by Russell, Wittgenstein and the logical positivists, that philosophical problems often turned on misunderstandings of syntax (or the closely related notion of ‘logical form’). Moreover, an idea that has been fruitfully developed since the pioneering work of Frege is that a proper understanding of syntax offers an important basis for any understanding of semantics, since the meaning of a complex expression is compositional, that is, built up from the meanings of its parts as determined by syntax. In the mid-twentieth century, philosophical interest in the systematic study of the syntax of natural language was heightened by Noam Chomsky’s work on the nature of syntactic rules and on the innateness of mental structures specific to the acquisition (or growth) of grammatical knowledge. This work formalized traditional work on grammatical categories within an approach to the theory of computability, and also revived proposals of traditional philosophical rationalists that many twentieth-century empiricists had regarded as bankrupt. Chomskian theories of grammar have become the focus of most contemporary work on syntax.

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