基于协同训练的文本蕴含识别

针对文本蕴含的训练数据不足的问题,该文提出了基于协同训练的文本蕴含识别方法。该方法利用少量已标注的蕴含数据和大量未标注数据进行协同训练。为此,该文利用改写视图和评估视图,从结构和非结构两个角度考察蕴含关系,并将语义树核分类器和基于统计特征的分类器应用于两个视图,同时利用协同训练的结果训练一个综合分类器,用于对新数据进行预测。实验表明,基于协同训练的蕴含识别方法能在少量训练数据的情况下获得较好的识别性能。

A Co-training Based Approach to Recognizing Textual Entailment

This paper introduces a co-training approach to recognizing textual entailment. In this approach, a small labeled entailment dataset as well as a large unlabeled one are employed for co-training, which aims at solving the lack of entailment data . Two different views, rewriting view and assessing view, are proposed to measure structural and non-structural entailment relations, likewise two classifiers, namely semantic tree kernel based classifier and statistical features based classifier, are applied to train under the two views respectively. For predication, a global classifier is built, trained by the results of co-training. Experiments show that the co-training based approach achieves a good performance in the case of a small training dataset.