增量学习算法用于域特定词汇的动态演变,其稳定性和可塑性分析
Mansi Jain1, Harmeet Kaur2, Bhavna Gupta3
1Department of Computer Science, Shyama Prasad Mukherji College for Women, University of Delhi, Delhi, India.
Scientific reports
|January 2, 2025
概括
本研究引入了一种增量学习算法,以高效地更新特定领域的词汇. 该方法保持了词汇相关性和有效性,用于诸如具有有限内存和处理需求的文本分类等任务.
科学领域:
- 自然语言处理自然语言处理.
- 信息检索 信息检索
- 机器学习 机器学习
背景情况:
- 对于信息检索和自然语言处理等领域,特定领域的词汇需要不断更新.
- 传统的方法需要从头开始重新培训,这是低效的.
- 增量学习通过更新现有知识而提供了替代方案,而不需要完全重新培训.
研究的目的:
- 介绍一种增量学习算法,用于更新特定领域的词汇.
- 介绍DocLib,一个用于存储数据和词汇足迹的档案.
- 评估更新词汇在下游任务如文本分类中的有效性.
主要方法:
- 开发了一个增量学习算法,用于词汇更新.
- 使用DocLib存档数据和词汇术语.
- 雇佣基于任务的评估,特别是文本分类,以衡量词汇的有效性.
- 使用新的算法评估词汇稳定性和可塑性.
- 测试了跨数据集的概括性,并与最先进的技术进行了比较.
主要成果:
- 拟议的算法确保了有限的内存和处理要求.
- 多次增量更新保持了词汇的相关性和有效性.
- 在跨数据集的域相关数据识别中实现了97.89%的准确性.
- 证明了吸收新知识的能力,同时保留旧的见解.
- 与基准数据集上最先进的技术相比,证实了有效性.
结论:
- 增量学习方法有效地更新特定领域的词汇.
- 该方法是高效的,具有有限的内存和处理需求.
- 该方法显示出强大的可通用性和各种研究领域的潜力,超出了分类范围.
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