Abstract:

Automated keyword extraction from agricultural database texts is a crucial step in achieving intelligent utilization and services. This study addresses the challenges faced by traditional keyword extraction methods, which struggle to mine deep semantic associations within texts, as well as the issues with semantic embedding-based approaches that are susceptible to semantic representation bias and dilution of key information. By innovating on the model, we propose a more precise keyword extraction method tailored for agricultural database texts. We enhance the semantic associations and edge weight accuracy of the TextRank word graph by incorporating co-occurring word analysis and construct a feature statistics module to extract candidate keywords. Simultaneously, we integrate the Bert-base-Chinese pre-trained model for vectorized encoding of texts and extract candidate keywords through vector similarity calculations. Finally, a multi-source fusion decision-making process is employed to generate the final keyword list by fusing and weighting the outputs from the two modules, along with word positions, resulting in a keyword extraction method(BWE-COW-TR) that combines BERT semantic embedding with TextRank word graph and co-occurring word analysis features. The precision(49.83%), recall(58.29%), and F1 score(0.5373) obtained from keyword extraction experiments conducted on an agricultural science and technology literature dataset using this method are all significantly higher than those of the baseline model. Its F1 score has improved by 70.90%, 51.74%, and 45.77% respectively compared to the F1 scores of KeyBERT, TF-IDF, and TextRank. The research results demonstrate that the proposed method outperforms the commonly used KeyBERT, TF-IDF, and TextRank methods in keyword extraction from agricultural database texts.

Key words: agricultural information, TextRank, BERT, co-occurrence, extraction, semantic embedding model