Reducing the Corpus Annotation Bottleneck for Natural Language Learning

自然语言处理(NLP)领域的进展目前受到创建新注释语料库的速度的限制，至少部分是这样。此外，有证据表明，要实现自动文本理解的下一级性能，将需要比目前可用的训练语料库大几个数量级的标注训练语料库。简而言之，在构建健壮、准确的NLP系统组件方面存在语料库标注瓶颈。因此，PI建议研究机器学习范例，这些范例将大大降低人工标注成本，同时保持或提高在所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等人，1994)和弱监督自举算法(如联合训练)在自然语言处理中的一组代表性问题上的应用，(2)确定这些方法在创建大型自然语言学习训练语料库期间减少人工标注负担的好处和局限性，以及(3)开发一个合作学习框架(Piells&amp；Cardie，2002)，该框架将主动学习和弱监督学习相结合，试图更有效地交互手动和自动语言标注工作。

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.