The Experts below are selected from a list of 92979 Experts worldwide ranked by ideXlab platform
Nuria Agell - One of the best experts on this subject based on the ideXlab platform.
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aggregating news reporting sentiment by means of hesitant Linguistic Terms
Modeling Decisions for Artificial Intelligence, 2020Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel SazAbstract:This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy Linguistic Terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in Terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering Linguistic Terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant Linguistic Terms can be considered well suited for expressing the tone of articles.
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MDAI - Aggregating News Reporting Sentiment by Means of Hesitant Linguistic Terms
Modeling Decisions for Artificial Intelligence, 2020Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel SazAbstract:This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy Linguistic Terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in Terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering Linguistic Terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant Linguistic Terms can be considered well suited for expressing the tone of articles.
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evaluating student internship fit using fuzzy Linguistic Terms and a fuzzy owa operator
IEEE International Conference on Fuzzy Systems, 2017Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, German Sanchezhernandez, Xari Rovira, Cecilio AnguloAbstract:Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy Linguistic Terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.
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FUZZ-IEEE - Evaluating student-internship fit using fuzzy Linguistic Terms and a fuzzy OWA operator
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Xari Rovira, Germán Sánchez-hernández, Cecilio AnguloAbstract:Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy Linguistic Terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.
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a consensus model for delphi processes with Linguistic Terms and its application to chronic pain in neonates definition
Applied Soft Computing, 2015Co-Authors: Nuria Agell, Llorenç Roselló, Christ Jan Van Ganzewinkel, Monica Sanchez, Francesc Prats, Peter AndriessenAbstract:Graphical abstractDisplay Omitted This paper proposes a new model of consensus based on Linguistic Terms to be implemented in Delphi processes. The model of consensus involves qualitative reasoning techniques and is based on the concept of entropy. The proposed model has the ability to reach consensus automatically without the need for either a moderator or a final interaction among panelists. In addition, it permits panelists to answer with different levels of precision depending on their knowledge on each question. The model defined has been used to establish the relevant features for the definition of a type of chronic disease. A real-case application conducted in the Department of Neonatology of Maxima Medical Center in The Netherlands is presented. This application considers the opinions of stakeholders of neonate health-care in order to reach a final consensual definition of chronic pain in neonates.
Marek Reformat - One of the best experts on this subject based on the ideXlab platform.
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question answering system with Linguistic Terms over rdf knowledge graphs
Systems Man and Cybernetics, 2020Co-Authors: Marek ReformatAbstract:Resource Description Framework (RDF) is an important way of representing data on the Web. Although RDF is a data format suitable for publishing individual pieces of information together with relations between them, it represents a challenging format for answering questions. Thus, a system and a user interface that are easy and intuitive for users to access and operate on RDF data are of significant importance.In this paper, we introduce a Question-Answering (QA) system that allows users to ask questions in English. The uniqueness of this system is its ability to answer questions containing Linguistic Terms, i.e., concepts such as SMALL, LARGE, or TALL. Those concepts are defined via membership functions drawn by users using a dedicated software designed for entering ‘shapes’ of these functions. The system is built based on an analogical problem solving approach, and is suitable for providing users with comprehensive answers. We demonstrate the capability of the proposed QA system by answering questions asked over two RDF stores: DBpedia and Wikidata.
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SMC - Question-Answering System with Linguistic Terms over RDF Knowledge Graphs
2020 IEEE International Conference on Systems Man and Cybernetics (SMC), 2020Co-Authors: Marek ReformatAbstract:Resource Description Framework (RDF) is an important way of representing data on the Web. Although RDF is a data format suitable for publishing individual pieces of information together with relations between them, it represents a challenging format for answering questions. Thus, a system and a user interface that are easy and intuitive for users to access and operate on RDF data are of significant importance.In this paper, we introduce a Question-Answering (QA) system that allows users to ask questions in English. The uniqueness of this system is its ability to answer questions containing Linguistic Terms, i.e., concepts such as SMALL, LARGE, or TALL. Those concepts are defined via membership functions drawn by users using a dedicated software designed for entering ‘shapes’ of these functions. The system is built based on an analogical problem solving approach, and is suitable for providing users with comprehensive answers. We demonstrate the capability of the proposed QA system by answering questions asked over two RDF stores: DBpedia and Wikidata.
Jennifer Nguyen - One of the best experts on this subject based on the ideXlab platform.
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aggregating news reporting sentiment by means of hesitant Linguistic Terms
Modeling Decisions for Artificial Intelligence, 2020Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel SazAbstract:This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy Linguistic Terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in Terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering Linguistic Terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant Linguistic Terms can be considered well suited for expressing the tone of articles.
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MDAI - Aggregating News Reporting Sentiment by Means of Hesitant Linguistic Terms
Modeling Decisions for Artificial Intelligence, 2020Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel SazAbstract:This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy Linguistic Terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in Terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering Linguistic Terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant Linguistic Terms can be considered well suited for expressing the tone of articles.
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evaluating student internship fit using fuzzy Linguistic Terms and a fuzzy owa operator
IEEE International Conference on Fuzzy Systems, 2017Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, German Sanchezhernandez, Xari Rovira, Cecilio AnguloAbstract:Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy Linguistic Terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.
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FUZZ-IEEE - Evaluating student-internship fit using fuzzy Linguistic Terms and a fuzzy OWA operator
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Xari Rovira, Germán Sánchez-hernández, Cecilio AnguloAbstract:Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy Linguistic Terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.
Dick Botteldooren - One of the best experts on this subject based on the ideXlab platform.
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fuzzy translation tool for Linguistic Terms
IEEE International Conference on Fuzzy Systems, 2004Co-Authors: Andy Verkeyn, Dick BotteldoorenAbstract:An automatic translation tool for Linguistic Terms is built. The Terms are represented by fuzzy sets and the translations are based on the similarity degree between those fuzzy sets. The tool is tested on 21 adverbs in 9 languages. The fuzzy sets are constructed with a probability based approach, based on data from an International study on the choice of appropriate Terms to label a noise annoyance scale. The results are in agreement with common sense translations. A detailed sensitivity analysis shows that the procedure is stable for many operator choices.
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Generating Membership Functions for a Noise Annoyance Model from Experimental Data
Soft Computing in Measurement and Information Acquisition, 2003Co-Authors: Andy Verkeyn, Dick Botteldooren, M. De Cock, Etienne KerreAbstract:The success of fuzzy expert systems could be mainly attributed to the inclusion of Linguistic Terms into their reasoning scheme. This allows reasoning about complex issues within a certain (tolerated) degree of imprecision. Hence, an important issue in the development of such systems is the choice of the membership functions that model the Linguistic Terms involved in the application. In this chapter we will describe several methods for the construction of these membership functions (which represent information) from measurements obtained in psychoLinguistic experiments. Special attention will be paid to the inclusive and the non-inclusive interpretation of Linguistic Terms. Secondly, these techniques are applied to data gathered in an International Annoyance Scaling Study, where the relationship between more than 20 different Linguistic Terms and their corresponding noise annoyance level was under survey.
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FUZZ-IEEE - Fuzzy translation tool for Linguistic Terms
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 1Co-Authors: Andy Verkeyn, Dick BotteldoorenAbstract:An automatic translation tool for Linguistic Terms is built. The Terms are represented by fuzzy sets and the translations are based on the similarity degree between those fuzzy sets. The tool is tested on 21 adverbs in 9 languages. The fuzzy sets are constructed with a probability based approach, based on data from an International study on the choice of appropriate Terms to label a noise annoyance scale. The results are in agreement with common sense translations. A detailed sensitivity analysis shows that the procedure is stable for many operator choices.
Albert Armisen - One of the best experts on this subject based on the ideXlab platform.
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aggregating news reporting sentiment by means of hesitant Linguistic Terms
Modeling Decisions for Artificial Intelligence, 2020Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel SazAbstract:This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy Linguistic Terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in Terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering Linguistic Terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant Linguistic Terms can be considered well suited for expressing the tone of articles.
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MDAI - Aggregating News Reporting Sentiment by Means of Hesitant Linguistic Terms
Modeling Decisions for Artificial Intelligence, 2020Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Angel SazAbstract:This paper focuses on analyzing the underlying sentiment of news articles, taken to be factual rather than comprised of opinions. The sentiment of each article towards a specific theme can be expressed in fuzzy Linguistic Terms and aggregated into a centralized sentiment which can be trended. This allows the interpretation of sentiments without conversion to numerical values. The methodology, as defined, maintains the range of sentiment articulated in each news article. In addition, a measure of consensus is defined for each day as the degree to which the articles published agree in Terms of the sentiment presented. A real case example is presented for a controversial event in recent history with the analysis of 82,054 articles over a three day period. The results show that considering Linguistic Terms obtain compatible values to numerical values, however in a more humanistic expression. In addition, the methodology returns an internal consensus among all the articles written each day for a specific country. Therefore, hesitant Linguistic Terms can be considered well suited for expressing the tone of articles.
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evaluating student internship fit using fuzzy Linguistic Terms and a fuzzy owa operator
IEEE International Conference on Fuzzy Systems, 2017Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, German Sanchezhernandez, Xari Rovira, Cecilio AnguloAbstract:Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy Linguistic Terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.
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FUZZ-IEEE - Evaluating student-internship fit using fuzzy Linguistic Terms and a fuzzy OWA operator
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jennifer Nguyen, Albert Armisen, Nuria Agell, Xari Rovira, Germán Sánchez-hernández, Cecilio AnguloAbstract:Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidate's interests, can be identified with fuzzy Linguistic Terms and a fuzzy OWA operator. A set of relevant positions aligned with a student's interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.
