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