Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest

Martinez‐Gil, Jorge

doi:10.1007/s10844-019-00561-0

Cited by 10 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, the idea of creating semantic similarity ensembles has sparked considerable interest [59,60]. Over the years, many methods based on different paradigms have been proposed [61,62,63,64,65]. These approaches have contributed to the ongoing exploration and development of semantic similarity measurement.…”

Section: Hybrid Methodsmentioning

confidence: 99%

30 Years of Research on Semantic Similarity Measurement

Sharma¹

2023

Preprint

View full text Add to dashboard Cite

Over the past three decades, researchers have been interested in measuring the semantic similarity between words or concepts in natural language processing. This interest has arisen due to the need for more effective techniques for information retrieval, text classification, and other tasks. The concept of semantic similarity refers to the degree of relatedness between two words or concepts based on their meaning or context. Semantic similarity measurement involves various methods such as knowledge-based, corpus-based, and hybrid methods. In this paper, we review the evolution of semantic similarity measurement over the past 30 years, including the different approaches used and their advantages and limitations. We also discuss the challenges in developing accurate semantic similarity metrics and highlight the emerging trends in this field, such as the use of deep learning techniques to improve the accuracy of semantic similarity measurement. Finally, we conclude by suggesting some directions for future research in this field.

show abstract

Section: Hybrid Methodsmentioning

confidence: 99%

30 Years of Research on Semantic Similarity Measurement

Sharma¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Early approaches for assessing the similarity relied on just textual analysis [25]. These techniques, while efficient, often struggle to capture the structural aspects of code, resulting in limited accuracy [12].…”

Section: Methodsmentioning

confidence: 99%

CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures

Martinez‐Gil¹

2017

Preprint

Self Cite

View full text Add to dashboard Cite

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches.

show abstract

“…To do that, we seek to aggregate modern similarity techniques strategically. The possible mistakes that a method could make lose importance on an ensemble of techniques that generally blur any of these mistakes [35]. In this way, only if all methods produce the same error does the aggregation lose its usefulness.…”

Section: Importance Of Symbolic Regressionmentioning

confidence: 99%

Sustainable semantic similarity assessment

Martinez‐Gil

Chaves-González

2022

IFS

Self Cite

View full text Add to dashboard Cite

The automatic semantic similarity assessment field has attracted much attention due to its impact on multiple areas of study. In addition, it is also relevant that recent advances in neural computation have taken the solutions to a higher stage. However, some inherent problems persist. For example, large amounts of data are still needed to train solutions, the interpretability of the trained models is not the most suitable one, and the energy consumption required to create the models seems out of control. Therefore, we propose a novel method to achieve significant results for a sustainable semantic similarity assessment, where accuracy, interpretability, and energy efficiency are equally important. We rely on a method based on multi-objective symbolic regression to generate a Pareto front of compromise solutions. After analyzing the output generated and comparing other relevant works published, our approach’s results seem to be promising.

show abstract

Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest

Cited by 10 publications

References 35 publications

30 Years of Research on Semantic Similarity Measurement

30 Years of Research on Semantic Similarity Measurement

CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures

Sustainable semantic similarity assessment

Contact Info

Product

Resources

About