Information extraction(IE) is a crucial subfield within natural language
processing. However, for the traditionally segmented approach to sentence
classification and Named Entity Recognition, the intricate interactions between
these individual subtasks remain largely uninvestigated. In this study, we
propose an integrative analysis, converging sentence classification with Named
Entity Recognition, with the objective to unveil and comprehend the mutual
reinforcement effect within these two information extraction subtasks. To
achieve this, 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. We
additionally implemented the SLG framework on single SC task. It yielded
superior accuracies compared to the baseline on two distinct Japanese SC
datasets. Notably, in the experiment of few-shot learning, SLG framework shows
much better performance than fine-tune method. These empirical findings
contribute additional evidence to affirm the efficacy of the SLG framework.

信息抽取是自然语言处理中一个关键领域，本研究提出了一个多任务学习方法，通过整合句子分类和命名实体识别，揭示了这两个信息抽取子任务之间的相互增强效应。结果显示，整合增强了句子分类和命名实体识别的性能，并且实验证明了该方法的有效性。