Part of speech tagging: a systematic review of deep learning and machine learning approaches
Abstract Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP too...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-01-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-022-00561-y |
| _version_ | 1818333181472931840 |
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| author | Alebachew Chiche Betselot Yitagesu |
| author_facet | Alebachew Chiche Betselot Yitagesu |
| author_sort | Alebachew Chiche |
| collection | DOAJ |
| description | Abstract Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging. |
| first_indexed | 2024-12-13T13:47:33Z |
| format | Article |
| id | doaj.art-2f6b5fd9a49648eda471cb6b6e8704b3 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-12-13T13:47:33Z |
| publishDate | 2022-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-2f6b5fd9a49648eda471cb6b6e8704b32022-12-21T23:43:21ZengSpringerOpenJournal of Big Data2196-11152022-01-019112510.1186/s40537-022-00561-yPart of speech tagging: a systematic review of deep learning and machine learning approachesAlebachew Chiche0Betselot Yitagesu1Department of Information Systems, College of Computing, Debre Berhan UniversityDepartment of Computer Science, College of Computing, Debre Berhan UniversityAbstract Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.https://doi.org/10.1186/s40537-022-00561-yMachine learningDeep learningHybrid approachPart of speechPart of speech taggingNLP |
| spellingShingle | Alebachew Chiche Betselot Yitagesu Part of speech tagging: a systematic review of deep learning and machine learning approaches Journal of Big Data Machine learning Deep learning Hybrid approach Part of speech Part of speech tagging NLP |
| title | Part of speech tagging: a systematic review of deep learning and machine learning approaches |
| title_full | Part of speech tagging: a systematic review of deep learning and machine learning approaches |
| title_fullStr | Part of speech tagging: a systematic review of deep learning and machine learning approaches |
| title_full_unstemmed | Part of speech tagging: a systematic review of deep learning and machine learning approaches |
| title_short | Part of speech tagging: a systematic review of deep learning and machine learning approaches |
| title_sort | part of speech tagging a systematic review of deep learning and machine learning approaches |
| topic | Machine learning Deep learning Hybrid approach Part of speech Part of speech tagging NLP |
| url | https://doi.org/10.1186/s40537-022-00561-y |
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