Reducing the Corpus Annotation Bottleneck for Natural Language Learning

自然语言处理（NLP）领域的进步目前受到限制，至少在一定程度上受到新标注语料库创建速度的限制。此外，有证据表明，要实现自动文本理解的下一个性能水平，需要比当前可用的有注释的训练语料库大几个数量级。简而言之，在构建鲁棒、准确的自然语言处理系统组件时，存在语料库标注瓶颈。因此，PI建议研究机器学习范式，这些范式将显著降低人工注释成本，同时保持或提高在获得的语料库上训练的自然语言学习算法的准确性。该项目将(1)研究主动学习（Cohn et al., 1994）和弱监督自举算法(如协同训练（Blum & Mitchell, 1998）在自然语言处理中的一系列代表性问题上的应用，(2)确定这些方法在为自然语言学习创建大型训练语料库期间减少手工注释负担方面的优点和局限性，(3)开发合作学习框架（Pierce & Cardie, 1998）。2002)，它结合了主动学习和弱监督学习，试图更有效地将手动和自动语言注释工作交叉起来。

Progress in the field of natural language processing (NLP) is currently limited, at least in part, by the speed with which new annotated corpora can be created. In addition, there is evidence that achieving the next level of performance in automated text understanding will require annotated training corpora that are orders of magnitude larger than those currently available. In short, there exists a corpus annotation bottleneck in building robust, accurate NLP system components. The PI proposes, therefore, to investigate machine learning paradigms that will significantly reduce human annotation costs while maintaining or improving the accuracy of the natural language learning algorithms that are trained on the acquired corpora. The project will (1) study the application of active learning (Cohn et al., 1994) and weakly supervised bootstrapping algorithms like co-training (Blum & Mitchell, 1998) on a set of representative problems in natural language processing, (2) identify the benefits and limitations of these approaches for reducing the manual annotation burden during the creation of large training corpora for natural language learning, and (3) develop a cooperative learning framework (Pierce & Cardie, 2002) that combines active and weakly supervised learning in an attempt to more effectively interleave manual and automated linguistic annotation efforts.