TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification
Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especial...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/22/3576 |
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| author | Carlos Sánchez-Antonio José E. Valdez-Rodríguez Hiram Calvo |
| author_facet | Carlos Sánchez-Antonio José E. Valdez-Rodríguez Hiram Calvo |
| author_sort | Carlos Sánchez-Antonio |
| collection | DOAJ |
| description | Recent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especially when working with small or imbalanced datasets. This paper introduces TTG-Text, a novel framework that strengthens graph-based text representation by integrating typical testors—a symbolic feature selection technique that refines feature importance while reducing dimensionality. Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. By combining symbolic algorithms with graph-based models, this hybrid approach offers a more interpretable, efficient, and high-performing solution for complex NLP tasks. |
| format | Article |
| id | doaj-art-836ea1c5f16b413daa327d0f5cfc5d37 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-836ea1c5f16b413daa327d0f5cfc5d372025-08-20T02:48:05ZengMDPI AGMathematics2227-73902024-11-011222357610.3390/math12223576TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved ClassificationCarlos Sánchez-Antonio0José E. Valdez-Rodríguez1Hiram Calvo2Cognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, MexicoCognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, MexicoCognitive Sciences Laboratory, Center for Computing Research, Instituto Politécnico Nacional, Mexico City 07738, MexicoRecent advancements in graph-based text representation, particularly with embedding models and transformers such as BERT, have shown significant potential for enhancing natural language processing (NLP) tasks. However, challenges related to data sparsity and limited interpretability remain, especially when working with small or imbalanced datasets. This paper introduces TTG-Text, a novel framework that strengthens graph-based text representation by integrating typical testors—a symbolic feature selection technique that refines feature importance while reducing dimensionality. Unlike traditional TF-IDF weighting, TTG-Text leverages typical testors to enhance feature relevance within text graphs, resulting in improved model interpretability and performance, particularly for smaller datasets. Our evaluation on a text classification task using a graph convolutional network (GCN) demonstrates that TTG-Text achieves a 95% accuracy rate, surpassing conventional methods and BERT with fewer required training epochs. By combining symbolic algorithms with graph-based models, this hybrid approach offers a more interpretable, efficient, and high-performing solution for complex NLP tasks.https://www.mdpi.com/2227-7390/12/22/3576graph-based text representationtypical testorstext classificationTF-IDFgraph convolutional networks (GCNs)natural language processing (NLP) |
| spellingShingle | Carlos Sánchez-Antonio José E. Valdez-Rodríguez Hiram Calvo TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification Mathematics graph-based text representation typical testors text classification TF-IDF graph convolutional networks (GCNs) natural language processing (NLP) |
| title | TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification |
| title_full | TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification |
| title_fullStr | TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification |
| title_full_unstemmed | TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification |
| title_short | TTG-Text: A Graph-Based Text Representation Framework Enhanced by Typical Testors for Improved Classification |
| title_sort | ttg text a graph based text representation framework enhanced by typical testors for improved classification |
| topic | graph-based text representation typical testors text classification TF-IDF graph convolutional networks (GCNs) natural language processing (NLP) |
| url | https://www.mdpi.com/2227-7390/12/22/3576 |
| work_keys_str_mv | AT carlossanchezantonio ttgtextagraphbasedtextrepresentationframeworkenhancedbytypicaltestorsforimprovedclassification AT joseevaldezrodriguez ttgtextagraphbasedtextrepresentationframeworkenhancedbytypicaltestorsforimprovedclassification AT hiramcalvo ttgtextagraphbasedtextrepresentationframeworkenhancedbytypicaltestorsforimprovedclassification |