Browsing by Author "Yang, Mingye"

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    Dataset "Aviation Product Named Entity Recognition (ApNER)"
    (Cranfield University, 2024-08-23) Yang, Mingye
    In order to verify the proposed model, this study used the aircraft design standard documents and airworthiness standard documents published by the European Aviation Safety Agency (EASA) and the US Electronic Federal Regulations (eCFR), as well as the open source aircraft design dataset AircraftVerse [38] as the corpus to construct a named entity recognition dataset for aviation products (ApNER).
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    ItemOpen Access
    Named entity recognition in aviation products domain based on BERT
    (Institute of Electrical and Electronics Engineers (IEEE), 2024-12-12) Yang, Mingye; Namoano, Bernadin; Farsi, Maryam; Ahmet Erkoyuncu, John
    The aviation products' manufacturing industry is undergoing a profound transformation towards intelligence, among which the construction of a knowledge graph specifically for the aviation field has become the core link in achieving cognitive intelligence. In the process of knowledge graph construction, named entity recognition (NER) is a key step and one of the main tasks of knowledge extraction. Given the high degree of specialisation of aviation product text data and the wide span of contextual information, existing models often perform poorly in entity extraction. This paper proposes a new Named Entity Recognition (NER) method specifically tailored for the aviation product field (BBC-Ap), introducing an innovative approach that leverages domain-specific ontologies and advanced deep learning algorithms to significantly enhance the accuracy and efficiency of entity extraction from complex technical documents. The first step of this method is to establish an ontology model of aviation products and annotate the relevant text data to form a dataset for training the named entity model. Next, it adopts a multi-level model structure based on BERT, in which BERT is used to generate word vector representations, a bidirectional long short-term memory network (BiLSTM) is used as an encoder to extract semantic features, and a conditional random field (CRF) is used as a decoder to achieve optimal label assignment. Through experiments on the constructed aviation product dataset, the model achieved a Precision value of 91.74%, a Recall value of 92.46%, and an F1 score of 92.1%, Compared with other baseline models, the F1-score is improved by 0.9% to 1.5%. At the same time, the model also performs well on standard datasets such as CoNLLpp, with a Precision value of 92.87%, a Recall value of 92.54%, and an F1-Score of 92.70%. Finally, the model was used to successfully construct a knowledge graph reflecting the relationships between aviation products in Neo4j, further demonstrating the effectiveness and practicality of the method.