Converting text into the structured query language (Text2SQL) is a research
hotspot in the field of natural language processing (NLP), which has broad
application prospects. In the era of big data, the use of databases has
penetrated all walks of life, in which the collected data is large in scale,
diverse in variety, and wide in scope, making the data query cumbersome and
inefficient, and putting forward higher requirements for the Text2SQL model. In
practical applications, the current mainstream end-to-end Text2SQL model is not
only difficult to build due to its complex structure and high requirements for
training data, but also difficult to adjust due to massive parameters. In
addition, the accuracy of the model is hard to achieve the desired result.
Based on this, this paper proposes a pipelined Text2SQL method: SPSQL. This
method disassembles the Text2SQL task into four subtasks--table selection,
column selection, SQL generation, and value filling, which can be converted
into a text classification problem, a sequence labeling problem, and two text
generation problems, respectively. Then, we construct data formats of different
subtasks based on existing data and improve the accuracy of the overall model
by improving the accuracy of each submodel. We also use the named entity
recognition module and data augmentation to optimize the overall model. We
construct the dataset based on the marketing business data of the State Grid
Corporation of China. Experiments demonstrate our proposed method achieves the
best performance compared with the end-to-end method and other pipeline
methods.

本文提出了一种基于管道的 Text2SQL 方法 SPSQL，将任务分解为表选择、列选择、SQL 生成和值填充四个子任务，采用不同的数据格式以提高模型精度，并使用命名实体识别模块和数据增强进行优化，通过实验得出在市场业务数据上的 SPSQL 方法比端到端方法和其他管道方法表现更好。

SPSQL: 基于逐步解析的文本到 SQL 生成框架

SPSQL: Step-by-step Parsing Based Framework for Text-to-SQL Generation

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.

本文提出了一种科技资源知识提取方法，基于单词混合和 GRU 模型，结合单词混合向量映射方法和自注意力机制，有效改善中文科技资源文本信息关系提取效果。