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Part-of-speech tagging accuracy for manufacturing process documents and knowledge

Fatemeh Besharati Moghaddam (UGent) , Angel J. Lopez (UGent) , Stijn De Vuyst (UGent) and Sidharta Gautama (UGent)
(2024) INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023. In Lecture notes in networks and systems 824. p.782-791
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Abstract
Adaptive guidance systems in manufacturing that support operators during the assembly process need to serve the right information at the right time. A conversational recommender system as the single point of contact between the operator and different sources of information, based on natural language processing, can be introduced to assist the operators. Natural language processing techniques can help to mine answers in text-based knowledge repositories as available in training documents, work instructions, and company procedures. Both the content as well as the style of writing in these documents are different from general language use and we examine the accuracy of part-of-speech tagging within this close domain of manufacturing. A benchmark dataset has been constructed based on four different classes of documents typical in the manufacturing domain. The dataset contains 1206 tokens divided over eight tag types. The accuracy of two open-source corpora, spaCy and NLTK, has been measured on this benchmark with an average accuracy of resp. 93% and 87%. The conclusion drawn is that pre-trained natural language libraries can effectively handle the specific contexts in the assembly domain based on the provided accuracy.
Keywords
Natural Language Processing, Part of Speech, Closed domain, Operator support

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MLA
Besharati Moghaddam, Fatemeh, et al. “Part-of-Speech Tagging Accuracy for Manufacturing Process Documents and Knowledge.” INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023, edited by Kohei Arai, vol. 824, Springer, 2024, pp. 782–91, doi:10.1007/978-3-031-47715-7_52.
APA
Besharati Moghaddam, F., Lopez, A. J., De Vuyst, S., & Gautama, S. (2024). Part-of-speech tagging accuracy for manufacturing process documents and knowledge. In K. Arai (Ed.), INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023 (Vol. 824, pp. 782–791). https://doi.org/10.1007/978-3-031-47715-7_52
Chicago author-date
Besharati Moghaddam, Fatemeh, Angel J. Lopez, Stijn De Vuyst, and Sidharta Gautama. 2024. “Part-of-Speech Tagging Accuracy for Manufacturing Process Documents and Knowledge.” In INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023, edited by Kohei Arai, 824:782–91. Springer. https://doi.org/10.1007/978-3-031-47715-7_52.
Chicago author-date (all authors)
Besharati Moghaddam, Fatemeh, Angel J. Lopez, Stijn De Vuyst, and Sidharta Gautama. 2024. “Part-of-Speech Tagging Accuracy for Manufacturing Process Documents and Knowledge.” In INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023, ed by. Kohei Arai, 824:782–791. Springer. doi:10.1007/978-3-031-47715-7_52.
Vancouver
1.
Besharati Moghaddam F, Lopez AJ, De Vuyst S, Gautama S. Part-of-speech tagging accuracy for manufacturing process documents and knowledge. In: Arai K, editor. INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023. Springer; 2024. p. 782–91.
IEEE
[1]
F. Besharati Moghaddam, A. J. Lopez, S. De Vuyst, and S. Gautama, “Part-of-speech tagging accuracy for manufacturing process documents and knowledge,” in INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023, Amsterdam, the Netherlands, 2024, vol. 824, pp. 782–791.
@inproceedings{01HDKAFF5TPJBN35YCYZHSGP1V,
  abstract     = {{Adaptive guidance systems in manufacturing that support operators during the assembly
process need to serve the right information at the right time. A conversational recommender
system as the single point of contact between the operator and different sources of information,
based on natural language processing, can be introduced to assist the operators. Natural language
processing techniques can help to mine answers in text-based knowledge repositories as available
in training documents, work instructions, and company procedures. Both the content as well as
the style of writing in these documents are different from general language use and we examine
the accuracy of part-of-speech tagging within this close domain of manufacturing. A benchmark
dataset has been constructed based on four different classes of documents typical in the manufacturing
domain. The dataset contains 1206 tokens divided over eight tag types. The accuracy of two
open-source corpora, spaCy and NLTK, has been measured on this benchmark with an average
accuracy of resp. 93% and 87%. The conclusion drawn is that pre-trained natural language libraries
can effectively handle the specific contexts in the assembly domain based on the provided accuracy.}},
  author       = {{Besharati Moghaddam, Fatemeh and Lopez, Angel J. and De Vuyst, Stijn and Gautama, Sidharta}},
  booktitle    = {{INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 3, INTELLISYS 2023}},
  editor       = {{Arai, Kohei}},
  isbn         = {{9783031477140}},
  issn         = {{2367-3370}},
  keywords     = {{Natural Language Processing,Part of Speech,Closed domain,Operator support}},
  language     = {{eng}},
  location     = {{Amsterdam, the Netherlands}},
  pages        = {{782--791}},
  publisher    = {{Springer}},
  title        = {{Part-of-speech tagging accuracy for manufacturing process documents and knowledge}},
  url          = {{http://doi.org/10.1007/978-3-031-47715-7_52}},
  volume       = {{824}},
  year         = {{2024}},
}
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