A critical survey on the use of Fuzzy Sets in Speech and Natural Language Processing

Carvalho, João Paulo; Batista, Fernando; Coheur, Luísa

doi:10.1109/fuzz-ieee.2012.6250803

Cited by 25 publications

(14 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A review of fuzzy approaches made in 2012 for natural language processing [35] indicated that there was a very low percentage of papers relating to fuzzy classification over all the papers published in the area of natural language processing. Also, the review indicated that there was a very low percentage of papers relating to natural language processing over all the application papers published in the area of fuzzy systems.…”

Section: Background Of Fuzzy Text Classificationmentioning

confidence: 99%

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

Liu

Burnap

Alorainy

et al. 2019

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

show abstract

Section: Background Of Fuzzy Text Classificationmentioning

confidence: 99%

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

Liu

Burnap

Alorainy

et al. 2019

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

show abstract

“…En las últimas décadas, la aplicación de conjuntos difusos se ha visto ignorada en el campo del procesamiento del lenguaje natural y del habla, y se ha cuestionado la utilidad y contribución de la lógica difusa en las aplicaciones relacionadas con el procesamiento de señales de voz [31]. No obstante, la evolución de la computación y contribuciones tecnológicas en los últimos años han favorecido al enfoque de lógica difusa para su utilización en diversos campos de estudio [32].…”

Section: Lógica Difusaunclassified

Gender classification based on voice signals using fuzzy models and optimization algorithms

Martinez

Espitia

2019

ITECKNE

View full text Add to dashboard Cite

This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model.

show abstract

“…In the area of text classification, a review of fuzzy approaches for natural language processing (NLP) was made in [13] in 2012, which highlighted that there was a very low percentage of papers relating to fuzzy approaches over all the papers published in the NLP area and that there were very few NLP related application papers published in the area of fuzzy systems. Following the publication of the above review paper, a number of fuzzy approaches have been proposed for various applications, since fuzzy approaches are more suitable to deal with the ambiguity and fuzziness of text.…”

Section: Background Of Fuzzy Text Classificationmentioning

confidence: 99%

Fuzzy rule based systems for gender classification from blog data

Liu

Cocea

2018

2018 Tenth International Conference on Advanced Computational Intelligence (ICACI)

View full text Add to dashboard Cite

Gender classification is a popular machine learning task, which has been involved in various application areas, such as business intelligence, access control and cyber security. In the context of information granulation, gender related information can be divided into three types, namely, biological information, vision based information and social network based information. In traditional machine learning, gender identification has been typically treated as a discriminative classification task, i.e. it is aimed at learning a classifier that discriminates between male and female. In this paper, we argue that it is not always appropriate to identify gender in the way of discriminative classification, especially when considering the case that both male and female people are of high diversity and thus individuals of different genders could have high similarity to each other in terms of their characteristics. In order to address the above issue, we propose the use of a fuzzy approach for generative classification of gender. In particular, we focus on gender classification based on social network information. We conduct an experiment study by using a blog data set, and compare the fuzzy approach with C4.5, Naive Bayes and Support Vector Machine in terms of classification performance. The results show that the fuzzy approach outperforms the other approaches and is also capable of capturing the diversity of both male and female people and dealing with the fuzziness in terms of gender identification.

show abstract

A critical survey on the use of Fuzzy Sets in Speech and Natural Language Processing

Cited by 25 publications

References 45 publications

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

Gender classification based on voice signals using fuzzy models and optimization algorithms

Fuzzy rule based systems for gender classification from blog data

Contact Info

Product

Resources

About