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The knowledge extraction task is to extract triple relations (headentity-relation-tail entity) from unstructured text data. The existingknowledge extraction methods are divided into "pipeline" method and jointextraction method. The "pipeline" method is to separate named entityrecognition and entity relationship extraction and use their own modules toextract them. Although this method has better flexibility, the training speedis slow. The learning model of joint extraction is an end-to-end modelimplemented by neural network to realize entity recognition and relationshipextraction at the same time, which can well preserve the association betweenentities and relationships, and convert the joint extraction of entities andrelationships into a sequence annotation problem. In this paper, we propose aknowledge extraction method for scientific and technological resources based onword mixture and GRU, combined with word mixture vector mapping method andself-attention mechanism, to effectively improve the effect of textrelationship extraction for Chinese scientific and technological resources.

Scientific and Technological Text Knowledge Extraction Method of based on Word Mixing and GRU