A comprehensive review of stacking methods for semantic similarity measurement
This article presents a comprehensive review of stacking methods commonly used to address the challenge of automatic semantic similarity measurement in the literature. Since more than two decades of research have left various semantic similarity measures, scientists and practitioners often find many...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827022000986 |
| _version_ | 1811199148993544192 |
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| author | Jorge Martinez-Gil |
| author_facet | Jorge Martinez-Gil |
| author_sort | Jorge Martinez-Gil |
| collection | DOAJ |
| description | This article presents a comprehensive review of stacking methods commonly used to address the challenge of automatic semantic similarity measurement in the literature. Since more than two decades of research have left various semantic similarity measures, scientists and practitioners often find many difficulties in choosing the best method to put into production. For this reason, a novel generation of strategies has been proposed to use basic semantic similarity measures using base estimators to achieve a better performance than could be gained from any of the semantic similarity measures. In this work, we analyze different stacking techniques, ranging from the classical algebraic methods to the most powerful ones based on hybridization, including blending, neural, fuzzy, and genetic-based stacking. Each technique excels in aspects such as simplicity, robustness, accuracy, interpretability, transferability, or a favorable combination of several of those aspects. The goal is that the reader can have an overview of the state-of-the-art in this field. |
| first_indexed | 2024-04-12T01:43:06Z |
| format | Article |
| id | doaj.art-a2b077747c4344d0a781560318729ba0 |
| institution | Directory Open Access Journal |
| issn | 2666-8270 |
| language | English |
| last_indexed | 2024-04-12T01:43:06Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj.art-a2b077747c4344d0a781560318729ba02022-12-22T03:53:08ZengElsevierMachine Learning with Applications2666-82702022-12-0110100423A comprehensive review of stacking methods for semantic similarity measurementJorge Martinez-Gil0Software Competence Center Hagenberg GmbH, Softwarepark 32a, 4232 Hagenberg, AustriaThis article presents a comprehensive review of stacking methods commonly used to address the challenge of automatic semantic similarity measurement in the literature. Since more than two decades of research have left various semantic similarity measures, scientists and practitioners often find many difficulties in choosing the best method to put into production. For this reason, a novel generation of strategies has been proposed to use basic semantic similarity measures using base estimators to achieve a better performance than could be gained from any of the semantic similarity measures. In this work, we analyze different stacking techniques, ranging from the classical algebraic methods to the most powerful ones based on hybridization, including blending, neural, fuzzy, and genetic-based stacking. Each technique excels in aspects such as simplicity, robustness, accuracy, interpretability, transferability, or a favorable combination of several of those aspects. The goal is that the reader can have an overview of the state-of-the-art in this field.http://www.sciencedirect.com/science/article/pii/S2666827022000986Meta-learningStackingSemantic similarity measurement |
| spellingShingle | Jorge Martinez-Gil A comprehensive review of stacking methods for semantic similarity measurement Machine Learning with Applications Meta-learning Stacking Semantic similarity measurement |
| title | A comprehensive review of stacking methods for semantic similarity measurement |
| title_full | A comprehensive review of stacking methods for semantic similarity measurement |
| title_fullStr | A comprehensive review of stacking methods for semantic similarity measurement |
| title_full_unstemmed | A comprehensive review of stacking methods for semantic similarity measurement |
| title_short | A comprehensive review of stacking methods for semantic similarity measurement |
| title_sort | comprehensive review of stacking methods for semantic similarity measurement |
| topic | Meta-learning Stacking Semantic similarity measurement |
| url | http://www.sciencedirect.com/science/article/pii/S2666827022000986 |
| work_keys_str_mv | AT jorgemartinezgil acomprehensivereviewofstackingmethodsforsemanticsimilaritymeasurement AT jorgemartinezgil comprehensivereviewofstackingmethodsforsemanticsimilaritymeasurement |