半监督学习和主动学习相结合的浅层语义分析

Combining Semi-Supervised Learning and Active Learning for Shallow Semantic Parsing

语义分析是基于内容的文本挖掘领域的重要技术和研究难点。有监督机器学习方法受限于标注语料的规模,在小规模标注样本中难以获取较高性能。本文面向浅层语义分析任务,采用一种新颖的半监督学习方法——直推式支持向量机,并结合其训练特点提出了基于主动学习的样本优化策略。实验表明,本文提出的浅层语义分析方法通过整合主动学习与半监督学习,在小规模标注样本环境中取得了良好的学习效果。

Semantic analysis is one of the fundamental and key problems in the research of content-based Text Mining. Most of supervised machine learning methods led to poor performance when work on limited tagged data. This paper investigated a novel semi supervised learning algorithm Transductive Support Vector Machine for shallow se- mantic parsing. An optimization strategy of selecting training instances, based on active learning, was integrated with TSVM. The experiment result shows that the method integrating TSVM and optimization strategy for shallow semantic parsing outperforms supervised methods on small tagged data.