Abstract
As an important part of information extraction, relationship extraction aims to extract the relationships between given entities from natural language text. Based on the pretraining model R-BERT, this paper proposes an entity relationship extraction method that integrates an entity dependency path and pretraining model, which generates a dependency parse tree by dependency parsing, obtains the dependency path of an entity pair via a given entity, and uses an entity dependency path to exclude information such as modifier chunks and useless entities in sentences. This model has achieved good F1 value performance on the SemEval2010 Task 8 dataset. Experiments on datasets show that dependency parsing can provide context information for models and improve performance.
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Data for this work were obtained from the web (accessed from www.semeval2.fbk.eu/semeval2.php).
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This work was supported by the National Defense Science and Technology Industrial Technology Research Project (JSQB2017206C002).
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Yin, B., Sun, Y. Relationship classification based on dependency parsing and the pretraining model. Soft Comput 26, 8575–8583 (2022). https://doi.org/10.1007/s00500-022-07195-5
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DOI: https://doi.org/10.1007/s00500-022-07195-5