TextNetTopics Pro是一个基于主题模型的短文本文本分类,通过集成语义和文档主题分发信息来进行短文本分类
Daniel Voskergian1, Burcu Bakir-Gungor2, Malik Yousef3,4
1Computer Engineering Department, Faculty of Engineering, Al-Quds University, Jerusalem, Palestine.
Frontiers in genetics
|October 23, 2023
概括
根据标题对科学文章进行分类是具有挑战性的,因为文本短. TextNetTopics Pro通过结合词汇特征和主题模型来改善分类,有效地解决数据稀疏性和不平衡的数据集.
科学领域:
- 计算语言学计算语言学
- 图书统计学 图书统计学
- 机器学习用于文本分析.
背景情况:
- 科学文献的数量呈指数级增长,需要自动分类.
- 短文本,如文章标题,由于数据稀疏和上下文有限,对传统的文本挖掘构成挑战.
- 像TextNetTopics这样的先前方法显示出有希望,但需要对短文本分类进行增强.
研究的目的:
- 评估TextNetTopics在短文本上的表现.
- 提出TextNetTopics Pro,这是一个用于短文本分类的新框架.
- 为了提高科学文章的分类准确度,使用标题.
主要方法:
- 探索了TextNetTopics对短文本的表现.
- 开发了TextNetTopics Pro,将词汇特征和主题分布从主题模型中结合起来.
- 对生物医学和计算机科学数据集的九个最先进的短文本主题模型进行了评估.
- 使用文章摘要和不使用文章摘要进行分类性能比较.
- 评估不平衡数据的稳定性,特别是用于药物诱导性肝损伤分类.
主要成果:
- 在短文本分类中,TextNetTopics Pro有效地减轻了数据稀疏性.
- 拟议的方法在不平衡的数据集上显示出强大的性能.
- 利用主题模型中的语义信息显著提高了机器学习分类器的性能.
- 对比评估表明,结合方法的有效性超过了基线方法.
结论:
- TextNetTopics Pro提供了一个可靠的框架,用于根据标题对科学文章进行分类.
- 利用主题模型和词汇特征对于克服短文分析中的挑战至关重要.
- 这种方法显示出在文献识别和科学信息检索方面的应用潜力很大.
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