Reducing the Corpus Annotation Bottleneck for Natural Language Learning

减少自然语言学习的语料库标注瓶颈

• 批准号: 0208028
• 负责人: Claire Cardie
• 金额: $ 50万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0208028&HistoricalAwards=false
• 关键词: Reducing; Corpus; Annotation; Bottleneck; Natural

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.

自然语言处理领域（NLP）领域的进展至少部分受到创建新注释的Corpora的速度的限制。此外，有证据表明，在自动化文本理解中达到了下一个级别的表现，需要带注释的培训语料库，这些培训语料库比当前可用的数量级要大。简而言之，在构建坚固，准确的NLP系统组件中存在语料库注释瓶颈。因此，PI建议调查机器学习范例，以显着降低人类注释成本，同时维持或提高对获得的Corpora培训的自然语言学习算法的准确性。该项目将（1）研究主动学习的应用（Cohn等，1994）和弱监督的引导算法（例如共同培训）（Blum＆Mitchell，1998）在自然语言处理中的一系列代表性问题上（（2）确定这些方法的益处和限制在自然语言处理中的代表性问题，并确定了这些方法的培训，以减少培训的培训，以减少工具的培训，以减少群体的创建工作（3）。 （Pierce＆Cardie，2002）结合了主动和弱监督的学习，以更有效地交织手动和自动化的语言注释工作。