Semantic Annotation Model and Method Based on Internet Open Dataset

Xin Gao, Yansong Wang, Fang Wang, Baoqun Zhang, Caie Hu, Jian Wang and Longfei Ma
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Xin Gao: State Grid Beijing Electric Power Company, China
Yansong Wang: State Grid Beijing Electric Power Company, China
Fang Wang: State Grid Beijing Electric Power Company, China
Baoqun Zhang: State Grid Beijing Electric Power Company, China
Caie Hu: State Grid Beijing Electric Power Company, China
Jian Wang: State Grid Beijing Electric Power Company, China
Longfei Ma: State Grid Beijing Electric Power Company, China

International Journal of Intelligent Information Technologies (IJIIT), 2025, vol. 21, issue 1, 1-19

Abstract: Traditional semantic annotation faces the problem of dataset diversity. Different fields and scenarios need to be specially annotated, and annotation work usually requires a lot of manpower and time investment. To meet these challenges, this paper deeply studies the semantic annotation model and method based on internet open datasets, aiming to improve annotation efficiency and accuracy and promote data resource sharing and utilization. This paper selects Common Crawl dataset to provide sufficient training samples; methods such as removing stop words and deduplication are used to preprocess data to improve data quality; a keyword extraction model based on heuristic rules and text context is constructed. In terms of semantic annotation model, this paper constructs a model based on Bidirectional Long Short-Term Memory (BiLSTM), which can make full use of the part-of-speech information of the corpus context, capture the part-of-speech features of the corpus, and generate semantic tags through supervised learning.

Date: 2025
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