  Information extraction(IE) is a crucial subfield within natural language
processing. In this study, we introduce a Sentence Classification and Named
Entity Recognition Multi-task (SCNM) approach that combines Sentence
Classification (SC) and Named Entity Recognition (NER). We develop a
Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia
dataset containing both SC and NER. Using a format converter, we unify input
formats and employ a generative model to generate SC-labels, NER-labels, and
associated text segments. We propose a Constraint Mechanism (CM) to improve
generated format accuracy. Our results show SC accuracy increased by 1.13
points and NER by 1.06 points in SCNM compared to standalone tasks, with CM
raising format accuracy from 63.61 to 100. The findings indicate mutual
reinforcement effects between SC and NER, and integration enhances both tasks'
performance.

Computation and Language