Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter
Owing to rapid advancements in information and communication technology, natural language processing (NLP) methods have attracted considerable attention. Estimating uncertainty is a well-established problem in NLP. Developing systems capable of automatically detecting uncertain terms within a natura...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/6628192 |
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| author | Lingaraj K. S. Supreeth Yerriswamy T. Dayananda P. Rohith S. Shruthi G. |
| author_facet | Lingaraj K. S. Supreeth Yerriswamy T. Dayananda P. Rohith S. Shruthi G. |
| author_sort | Lingaraj K. |
| collection | DOAJ |
| description | Owing to rapid advancements in information and communication technology, natural language processing (NLP) methods have attracted considerable attention. Estimating uncertainty is a well-established problem in NLP. Developing systems capable of automatically detecting uncertain terms within a natural language is critical for developing effective text-analysis algorithms. Consequently, several NLP tools have been developed for this purpose. However, there are several obstacles to the design of fast and effective NLP technologies that effectively analyze ambiguous terms in natural languages. This work focuses on a particular method for detecting uncertainty, termed progressive detection, which can be accomplished using a dynamic probabilistic graphical model to solve these challenges. We iteratively upgraded the Kalman Filter to Labeled Variable Dimension Kalman Filter (LVDKF) based on observations from real datasets. The LVDKF learns two basic features from positive and negative words. The formula was then derived using a dynamic detection system, designed based on conditional probability. Finally, we simulated progressive detection experimental conditions and assessed the LVDKF on two publicly available datasets. It performs better than the baseline methods in these tests, which shows that it is effective at detecting changes over time. |
| format | Article |
| id | doaj-art-5977996821134c67955d532ea297172d |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-5977996821134c67955d532ea297172d2025-02-03T06:51:37ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/6628192Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman FilterLingaraj K.0S. Supreeth1Yerriswamy T.2Dayananda P.3Rohith S.4Shruthi G.5Rao Bahadur Y. Mahabaleswarappa Engineering CollegeSchool of Computer Science & EngineeringSchool of Computer Science & EngineeringDepartment of Information TechnologyDepartment of Electronics & Communication EngineeringSchool of Computer Science & EngineeringOwing to rapid advancements in information and communication technology, natural language processing (NLP) methods have attracted considerable attention. Estimating uncertainty is a well-established problem in NLP. Developing systems capable of automatically detecting uncertain terms within a natural language is critical for developing effective text-analysis algorithms. Consequently, several NLP tools have been developed for this purpose. However, there are several obstacles to the design of fast and effective NLP technologies that effectively analyze ambiguous terms in natural languages. This work focuses on a particular method for detecting uncertainty, termed progressive detection, which can be accomplished using a dynamic probabilistic graphical model to solve these challenges. We iteratively upgraded the Kalman Filter to Labeled Variable Dimension Kalman Filter (LVDKF) based on observations from real datasets. The LVDKF learns two basic features from positive and negative words. The formula was then derived using a dynamic detection system, designed based on conditional probability. Finally, we simulated progressive detection experimental conditions and assessed the LVDKF on two publicly available datasets. It performs better than the baseline methods in these tests, which shows that it is effective at detecting changes over time.http://dx.doi.org/10.1155/2024/6628192 |
| spellingShingle | Lingaraj K. S. Supreeth Yerriswamy T. Dayananda P. Rohith S. Shruthi G. Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter Applied Computational Intelligence and Soft Computing |
| title | Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter |
| title_full | Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter |
| title_fullStr | Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter |
| title_full_unstemmed | Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter |
| title_short | Progressive Detection of Uncertainty in Natural Language Processing Using a Labeled Variable Dimension Kalman Filter |
| title_sort | progressive detection of uncertainty in natural language processing using a labeled variable dimension kalman filter |
| url | http://dx.doi.org/10.1155/2024/6628192 |
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