A label semantics approach to linguistic hedges

Lewis, Martha; Lawry, Jonathan

doi:10.1016/j.ijar.2014.01.006

Cited by 8 publications

(8 citation statements)

References 32 publications

(110 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lakoff's approach was taken as a starting point in future studies of hedging (Lewis & Lawry, 2014;Rosanti & Jaelani, 2015). Later, the concept of hedging has been further developed in the course of linguistic pragmatics where its semantic content has considerably expanded.…”

Section: Post-lakoff Approaches To Hedgingmentioning

confidence: 99%

Linguistic Hedging In Interpersonal Communication

Vlasyan¹

2019

The European Proceedings of Social and Behavioural Sciences

View full text Add to dashboard Cite

show abstract

Section: Post-lakoff Approaches To Hedgingmentioning

confidence: 99%

Linguistic Hedging In Interpersonal Communication

Vlasyan¹

2019

The European Proceedings of Social and Behavioural Sciences

View full text Add to dashboard Cite

show abstract

“…De Cock and Kerre [10] presented the first approach to model hedges by fuzzy relations. Recently, Lewis and Lawry [29] represented linguistic hedges based on the label semantics framework. The approach is defined with both unmodified prototypes and differing prototypes.…”

Section: Compared With the Existing Techniques Of Modeling Hedgesmentioning

confidence: 99%

“…The powering hedges are usually criticized because they are arbitrary and semantically ungrounded. The proposed model, the fuzzy relation-based model [10] and the label semantic model [29]] have a clear semantic grounding. The label semantic model [29] highly depends on the threshold and its computational process is much more complex than that of the proposed model.…”

Section: Compared With the Existing Techniques Of Modeling Hedgesmentioning

confidence: 99%

“…The proposed model, the fuzzy relation-based model [10] and the label semantic model [29]] have a clear semantic grounding. The label semantic model [29] highly depends on the threshold and its computational process is much more complex than that of the proposed model. The proposed model and the fuzzy relation-based model [10] utilize piece-wise linear membership functions for representing semantics and thus the application is simple.…”

Section: Compared With the Existing Techniques Of Modeling Hedgesmentioning

confidence: 99%

See 1 more Smart Citation

Linguistic terms with weakened hedges: A model for qualitative decision making under uncertainty

Wang

Zhang

Zeng

2018

Information Sciences

View full text Add to dashboard Cite

When expressing the experts' opinions in qualitative decision making (QDM), linguistic hedges can be considered to modify the force expressed by a predefined linguistic term. If an expert is not sure to select one term, weakened hedges would be a natural way to express the uncertainty. This is usually implemented by using a hedge to modify the most possible term, like the expression "more or less good". To model the uncertainty implied by hedges in QDM, this paper presents a novel linguistic representational and computational model in which the linguistic expressions take the form of a weakened hedge and a linguistic term, which is named as linguistic term with weakened hedge (LTWH). The syntax of LTWHs is defined by a set of hedges and a set of linguistic terms. The semantics of a LTWH is determined, objectively, based on the semantics of the term and a similarity measure of the reference domain. Accordingly, the negation, order relations and some basic operations of LTWHs are defined. To illustrate the effectiveness of LTWHs in granular computing, the connection to some multi-granularity linguistic models is exploited and a process for unifying multi-granularity linguistic information is developed. The major contritions of this paper are: (1) The proposed model enables a new manner to express and operate uncertain linguistic information in QDM; (2) it possesses clear syntax and semantics and the computational results are very interpretable; and (3) the proposed solution of multi-granularity linguistic unification maintains the semantics of the original linguistic information.

show abstract

“…For this purpose, models of linguistic modifiers are developed. In semantic models, the linguistic modifier describes the significance measure of the primary term or hedging threshold [6][7][8]. Then the conditions of partition are associated with determining the hedging threshold, and selection is based on the relationships of semantic ordering [6].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Classification rule hierarchical tuning with linguistic modification based on solving fuzzy relational equations

Rakytyanska

2018

EEJET

View full text Add to dashboard Cite

Розроблено підхід, який поєднує семантичне навчання, гранулярне розбиття та розв'язання нечітких реляційних рівнянь для побудови точних та інтерпретабельних правил. Запропоновано сполучену нечітку модель прямого логічного виведення на основі первинних правил з гранулярними параметрами. Розроблено метод ієрархічного налаштування з лінгвістичною модифікацією на основі розв'язання нечітких реляційних рівнянь, що скорочує час навчанняКлючові слова: ієрархічне налаштування, класифікаційні нечіткі бази знань, розв'язання нечітких реляційних рівнянь Разработан подход, который объединяет семантическое обучение, гранулярное разбиение и решение нечетких реляционных уравнений для построения точных и интерпретабельных правил. Предложена составная нечеткая модель прямого логического вывода на основе первичных правил с гранулярными параметрами. Разработан метод иерархической настройки с лингвистической модификацией на основе решения нечетких реляционных уравнений, что сокращает время обученияКлючевые слова: иерархическая настройка, классификационные нечеткие базы знаний, решение нечетких реляционных уравнений UDC 681.5.015:007

show abstract

A label semantics approach to linguistic hedges

Cited by 8 publications

References 32 publications

Linguistic Hedging In Interpersonal Communication

Linguistic Hedging In Interpersonal Communication

Linguistic terms with weakened hedges: A model for qualitative decision making under uncertainty

Classification rule hierarchical tuning with linguistic modification based on solving fuzzy relational equations

Contact Info

Product

Resources

About