Semantic Categories: Uncertainty and Similarity
This paper addresses understanding and categorizing language by using Markov categories to establish a mathematical framework for semantic concepts. This framework enables us to measure the semantic similarity between linguistic expressions within a given text. Furthermore, this approach enables the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/29/6/106 |
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| _version_ | 1846103805409624064 |
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| author | Ares Fabregat-Hernández Javier Palanca Vicent Botti |
| author_facet | Ares Fabregat-Hernández Javier Palanca Vicent Botti |
| author_sort | Ares Fabregat-Hernández |
| collection | DOAJ |
| description | This paper addresses understanding and categorizing language by using Markov categories to establish a mathematical framework for semantic concepts. This framework enables us to measure the semantic similarity between linguistic expressions within a given text. Furthermore, this approach enables the measurement and control of uncertainty in language categorization and the creation of metrics for evaluating semantic similarity. We provide use cases to demonstrate how the proposed methods can be applied and computed, focusing on their interpretability and the universality of categorical constructions. This work contributes to the field by offering a novel perspective on semantic similarity and uncertainty metrics in language processing, generating criteria to automate their computation. |
| format | Article |
| id | doaj-art-a1d857318eb54c76bea1d568ade05f01 |
| institution | Kabale University |
| issn | 1300-686X 2297-8747 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj-art-a1d857318eb54c76bea1d568ade05f012024-12-27T14:38:26ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472024-11-0129610610.3390/mca29060106Semantic Categories: Uncertainty and SimilarityAres Fabregat-Hernández0Javier Palanca1Vicent Botti2Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, SpainValencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, SpainValencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, SpainThis paper addresses understanding and categorizing language by using Markov categories to establish a mathematical framework for semantic concepts. This framework enables us to measure the semantic similarity between linguistic expressions within a given text. Furthermore, this approach enables the measurement and control of uncertainty in language categorization and the creation of metrics for evaluating semantic similarity. We provide use cases to demonstrate how the proposed methods can be applied and computed, focusing on their interpretability and the universality of categorical constructions. This work contributes to the field by offering a novel perspective on semantic similarity and uncertainty metrics in language processing, generating criteria to automate their computation.https://www.mdpi.com/2297-8747/29/6/106category theoryMarkov categoriesnatural language processingexplainable artificial intelligence |
| spellingShingle | Ares Fabregat-Hernández Javier Palanca Vicent Botti Semantic Categories: Uncertainty and Similarity Mathematical and Computational Applications category theory Markov categories natural language processing explainable artificial intelligence |
| title | Semantic Categories: Uncertainty and Similarity |
| title_full | Semantic Categories: Uncertainty and Similarity |
| title_fullStr | Semantic Categories: Uncertainty and Similarity |
| title_full_unstemmed | Semantic Categories: Uncertainty and Similarity |
| title_short | Semantic Categories: Uncertainty and Similarity |
| title_sort | semantic categories uncertainty and similarity |
| topic | category theory Markov categories natural language processing explainable artificial intelligence |
| url | https://www.mdpi.com/2297-8747/29/6/106 |
| work_keys_str_mv | AT aresfabregathernandez semanticcategoriesuncertaintyandsimilarity AT javierpalanca semanticcategoriesuncertaintyandsimilarity AT vicentbotti semanticcategoriesuncertaintyandsimilarity |