A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text
With the increasing amount of textual information in the Internet, smart semantic comprehension is a practical demand. Among, automatic annotation for semantic roles remains the fundamental part for effective semantic comprehension. Although machine learning-based methods had received much attention...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10264071/ |
| _version_ | 1827800267011129344 |
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| author | Li Lei Hao Wang |
| author_facet | Li Lei Hao Wang |
| author_sort | Li Lei |
| collection | DOAJ |
| description | With the increasing amount of textual information in the Internet, smart semantic comprehension is a practical demand. Among, automatic annotation for semantic roles remains the fundamental part for effective semantic comprehension. Although machine learning-based methods had received much attention in recent years, they mostly divided each sentences into separable parts for calculation. To deal with such challenge, this paper introduces multilabel learning to propose a novel automatic annotation method for semantic roles in English text. In the semantic representation of words, the method uses convolutional neural networks to extract local feature information of words from the character level. Such design can alleviate the problem of inconspicuous semantic features caused by random initialization of unregistered words. Secondly, in the process of implication recognition, by combining the interactive attention mechanism to construct a capsule for each implication relation separately, the recognition of the final implication relation is completed in the way of categorical learning. At last, some experiments are conducted on real-world data to verify the proposed method with being compared with several typical relevant methods. The obtained results show that the proposal achieves better Macro-F1 results on eight datasets compared to seven algorithms. Besides, the proposal also performs better than others in the sensitivity testing, as its performance can remain stable with the increase of noise input. In summary, the proposal can achieve good results and show strong capability in semantic role labeling tasks. |
| first_indexed | 2024-03-11T20:05:55Z |
| format | Article |
| id | doaj.art-dd2e7b1e17cf4780856cc7e5dcffd310 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T20:05:55Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dd2e7b1e17cf4780856cc7e5dcffd3102023-10-03T23:00:32ZengIEEEIEEE Access2169-35362023-01-011110622010623110.1109/ACCESS.2023.331938410264071A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English TextLi Lei0Hao Wang1https://orcid.org/0000-0002-7096-0889School of General Education, Hunan University of Information Technology, Changsha, ChinaSchool of Foreign Language, Changsha Normal University, Changsha, ChinaWith the increasing amount of textual information in the Internet, smart semantic comprehension is a practical demand. Among, automatic annotation for semantic roles remains the fundamental part for effective semantic comprehension. Although machine learning-based methods had received much attention in recent years, they mostly divided each sentences into separable parts for calculation. To deal with such challenge, this paper introduces multilabel learning to propose a novel automatic annotation method for semantic roles in English text. In the semantic representation of words, the method uses convolutional neural networks to extract local feature information of words from the character level. Such design can alleviate the problem of inconspicuous semantic features caused by random initialization of unregistered words. Secondly, in the process of implication recognition, by combining the interactive attention mechanism to construct a capsule for each implication relation separately, the recognition of the final implication relation is completed in the way of categorical learning. At last, some experiments are conducted on real-world data to verify the proposed method with being compared with several typical relevant methods. The obtained results show that the proposal achieves better Macro-F1 results on eight datasets compared to seven algorithms. Besides, the proposal also performs better than others in the sensitivity testing, as its performance can remain stable with the increase of noise input. In summary, the proposal can achieve good results and show strong capability in semantic role labeling tasks.https://ieeexplore.ieee.org/document/10264071/Multi-label learningsemantic comprehensionautomatic annotationdeep neural networks |
| spellingShingle | Li Lei Hao Wang A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text IEEE Access Multi-label learning semantic comprehension automatic annotation deep neural networks |
| title | A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text |
| title_full | A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text |
| title_fullStr | A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text |
| title_full_unstemmed | A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text |
| title_short | A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text |
| title_sort | multilabel learning based automatic annotation method for semantic roles in english text |
| topic | Multi-label learning semantic comprehension automatic annotation deep neural networks |
| url | https://ieeexplore.ieee.org/document/10264071/ |
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