BERT-based knowledge extraction method of unstructured domain text
This addresses the need for efficient knowledge extraction in specific domains like insurance to reduce manual labor, but it is incremental as it adapts an existing BERT-based method.
The paper tackles the problem of automatically extracting entities and relations from unstructured domain texts, such as insurance clauses, to aid knowledge graph construction, achieving good performance in tests.
With the development and business adoption of knowledge graph, there is an increasing demand for extracting entities and relations of knowledge graphs from unstructured domain documents. This makes the automatic knowledge extraction for domain text quite meaningful. This paper proposes a knowledge extraction method based on BERT, which is used to extract knowledge points from unstructured specific domain texts (such as insurance clauses in the insurance industry) automatically to save manpower of knowledge graph construction. Different from the commonly used methods which are based on rules, templates or entity extraction models, this paper converts the domain knowledge points into question and answer pairs and uses the text around the answer in documents as the context. The method adopts a BERT-based model similar to BERT's SQuAD reading comprehension task. The model is fine-tuned. And it is used to directly extract knowledge points from more insurance clauses. According to the test results, the model performance is good.