Multilingual Access to Document Databases

2013 ◽  
Author(s):  
Steven A. Pollitt ◽  
Geoff Ellis
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Expert Systems ◽  
Document Retrieval ◽  
Optimal Solutions ◽  
End User ◽  
Natural Language Query ◽  
Document Databases

This paper examines the reasons why approaches to facilitate document retrieval which apply AI (Artificial Intelligence) or Expert Systems techniques, relying on so-called "natural language" query statements from the end-user will result in sub-optimal solutions. It does so by reflecting on the nature of language and the fundamental problems in document retrieval.

Expert Systems in Distance Education

2018 ◽  
pp. 1410-1423
Author(s):  
Duygu Mutlu-Bayraktar ◽  
Esad Esgin
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Natural Language ◽  
Expert Systems ◽  
Expert Knowledge ◽  
Computer Systems ◽  
Educational Environments ◽  
Artificial Environment ◽  
Present Solution ◽  
Definition Of

Computers have been used in educational environments to carry out applications that need expertise, such as compiling, storing, presentation, and evaluation of information. In some teaching environments that need expert knowledge, capturing and imitating the knowledge of the expert in an artificial environment and utilizing computer systems that have the ability to communicate with people using natural language might reduce the need for the expert and provide fast results. Expert systems are a study area of artificial intelligence and can be defined as computer systems that can approach a problem for which an answer is being sought like an expert and present solution recommendations. In this chapter, the definition of expert systems and their characteristics, information about the expert systems in teaching environments, and especially their utilization in distance education are given.

scholarly journals An End-to-End Conversational Second Screen Application for TV Program Discovery

AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 73-89 ◽  
Author(s):  
Peter Z. Yeh ◽  
Deepak Ramachandran ◽  
Benjamin Douglas ◽  
Adwait Ratnaparkhi ◽  
William Jarrold ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
User Studies ◽  
Second Phase ◽  
End User ◽  
Target Domain ◽  
Second Screen ◽  
Tv Program ◽  
Two Phases ◽  
Core Functionality

In this article, we report on a multiphase R&D effort to develop a conversational second screen application for TV program discovery. Our goal is to share with the community the breadth of artificial intelligence (AI) and natural language (NL) technologies required to develop such an application along with learnings from target end-users. We first give an overview of our application from the perspective of the end-user. We then present the architecture of our application along with the main AI and NL components, which were developed over multiple phases. The first phase focuses on enabling core functionality such as effectively finding programs matching the user’s intent. The second phase focuses on enabling dialog with the user. Finally, we present two user studies, corresponding to these two phases. The results from both studies demonstrate the effectiveness of our application in the target domain.

Expert Systems in Distance Education

2015 ◽  
pp. 136-148 ◽  
Author(s):  
Duygu Mutlu-Bayraktar ◽  
Esad Esgin
Keyword(s):  
Artificial Intelligence ◽  
Distance Education ◽  
Natural Language ◽  
Expert Systems ◽  
Expert Knowledge ◽  
Computer Systems ◽  
Educational Environments ◽  
Artificial Environment ◽  
Present Solution ◽  
Definition Of

Computers have been used in educational environments to carry out applications that need expertise, such as compiling, storing, presentation, and evaluation of information. In some teaching environments that need expert knowledge, capturing and imitating the knowledge of the expert in an artificial environment and utilizing computer systems that have the ability to communicate with people using natural language might reduce the need for the expert and provide fast results. Expert systems are a study area of artificial intelligence and can be defined as computer systems that can approach a problem for which an answer is being sought like an expert and present solution recommendations. In this chapter, the definition of expert systems and their characteristics, information about the expert systems in teaching environments, and especially their utilization in distance education are given.

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).

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

Sign in / Sign up

Export Citation Format

Share Document