Human Hedge Perception – and its Application in Fuzzy Semantic Similarity Measures

Adel, Naeemeh; Crockett, Keeley; Crispin, Alan; Carvalho, João Paulo; Chandran, David

doi:10.1109/fuzz-ieee.2019.8858880

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…FUSE is an ontology-based similarity measure that uses Interval Type-2 Fuzzy Sets to model relationships between categories of human perception-based words. Several versions of FUSE (FUSE_1.0 -FUSE_4.0) have been developed, investigating the presence of linguistic hedges, the expansion of fuzzy categories and their use in natural language, and the introduction of the fuzzy influence factor [14,15,16]. FUSE has been compared to several state-of-the-art, STSM which do not consider the presence of fuzzy words in the similarity calculation.…”

Section: A Semantic and Fuzzy Semantic Similarity Measuresmentioning

confidence: 99%

An Investigation into Fuzzy Negation in Semantic Similarity Measures

Adel,

Crockett,

Carvalho

2023

2023 IEEE International Conference on Fuzzy Systems (FUZZ)

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Machine computation of semantic similarity between short texts aims to approximate human measurements of similarity, often influenced by context, domain knowledge, and life experiences. Logical negation in natural language plays an important role as it can change the polarity of meaning within a sentence, yet it is a complex problem for semantic similarity measures to identify and measure. This paper investigates the impact of logical negation on determining fuzzy semantic similarity between short texts containing fuzzy words. A methodology is proposed to interpret the implications of a negation word on a fuzzy word within the context of a user utterance. Three known fuzzy logical not operators proposed by Zadeh, Yager and Sugeno are incorporated into a fuzzy semantic similarity measure called FUSE. Experiments are conducted on a sample dataset of short text inputs captured through human engagement with a dialogue system. Results show that Yager's weighted operator is the most suitable for achieving a matching threshold of 90.47% accuracy. This finding has significant implications for the field of semantic similarity measures. It provides a more accurate way to measure the similarity of short texts that contain fuzzy words combined with logical negation. Whilst validation of the approach on more substantial datasets is required, this study contributes to a better understanding of how to account for logical negation in fuzzy semantic similarity measures and provides a valuable methodology for future research in this area.

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Section: A Semantic and Fuzzy Semantic Similarity Measuresmentioning

confidence: 99%

An Investigation into Fuzzy Negation in Semantic Similarity Measures

Adel,

Crockett,

Carvalho

2023

2023 IEEE International Conference on Fuzzy Systems (FUZZ)

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“…There exists an extensive literature on measuring short text similarity for English [11][12][13], whereas lesser work has been done for Hindi in this domain. As this thesis is based upon using Corpus as a knowledge base, this section demonstrates the related literature that uses different resources.…”

Section: Related Workmentioning

confidence: 99%

An Artificial Intelligence Approach for Word Semantic Similarity Measure of Hindi Language

2021

KSII TIIS

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AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.

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“…The FUSE measure tackled the issue of uncertainty of human judgement [17] by modelling fuzzy words using Interval Type-2 fuzzy sets, originally proposed by Hao and Mendel [17]. FUSE was successfully evaluated and extended to include hedge words in [18].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Influence in Fuzzy Semantic Similarity Measures

Adel

Crockett

Carvalho

et al. 2021

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The field of Computing with Words has been pivotal in the development of fuzzy semantic similarity measures. Fuzzy semantic similarity measures allow the modelling of words in a given context with a tolerance for the imprecise nature of human perceptions. In this work, we look at how this imprecision can be addressed with the use of fuzzy semantic similarity measures in the field of natural language processing. A fuzzy influence factor is introduced into an existing measure known as FUSE. FUSE computes the similarity between two short texts based on weighted syntactic and semantic components in order to address the issue of comparing fuzzy words that exist in different word categories. A series of empirical experiments investigates the effect of introducing a fuzzy influence factor into FUSE across a number of short text datasets. Comparisons with other similarity measures demonstrates that the fuzzy influence factor has a positive effect in improving the correlation of machine similarity judgments with similarity judgments of humans.

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Human Hedge Perception – and its Application in Fuzzy Semantic Similarity Measures

Cited by 3 publications

References 20 publications

An Investigation into Fuzzy Negation in Semantic Similarity Measures

An Investigation into Fuzzy Negation in Semantic Similarity Measures

An Artificial Intelligence Approach for Word Semantic Similarity Measure of Hindi Language

Fuzzy Influence in Fuzzy Semantic Similarity Measures

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