铝硅合金实体关系抽取数据集的构建方法

针对材料领域没有适合材料实体关系抽取技术研究工作的公开数据集这一问题，通过研究高硅铝合金喷射沉积文献提出铝硅合金实体关系抽取数据集的构建方法. 在材料领域专家的指导下制定铝硅合金实体关系抽取数据集的构建标准，并根据构建标准对收集的数据进行实体标注和关系标注. 在标注完成后，通过数据预处理生成铝硅合金实体关系抽取数据集. 通过实体关系联合抽取模型进行实验，验证该数据集可以应用于实体关系抽取任务. 与公开数据集相比，材料数据集句子的语义和语法更为复杂，长句更多，导致实体关系联合抽取模型在材料数据集上的表现略差. 针对上述问题，在实体关系联合抽取模型上加入自注意力机制，使该模型整体的F1值提高了约5.8%. 该数据集的构建方法具有普适性，可以通过该构建方法构建材料数据集.

Construction method of extraction dataset of Al-Si alloy entity relationship

At present, there is no public dataset suitable for the research work of material entity relationship extraction technology in the field of materials. Aiming at the above problem, the construction method of aluminum-silicon alloy entity relationship extraction dataset was proposed through the literature of high-silicon aluminum alloy spray deposition. The construction standards of the aluminum-silicon alloy entity relationship extraction dataset were formulated under the guidance of experts in the material field, and the collected data were marked with entities and relationships according to the construction standards. After the annotation was completed, the aluminum-silicon alloy entity relationship extraction dataset was generated through data preprocessing. Experiments were conducted through the entity-relationship joint extraction model to verify that the dataset can be applied to entity-relationship extraction tasks. Compared with the public dataset, the semantics and grammar of the sentence in the material dataset were more complicated, and there were more long sentences, which led to a slightly worse performance of the entity relationship joint extraction model on the material dataset. Therefore, a self-attention mechanism was added to the entity relationship joint extraction model, which increased the overall F1 value by about 5.8%. The method of constructing the dataset is universal, and the material dataset can be constructed by the construction method.