Testing and improving methods for efficient annotation through the construction of a large parsed corpus

标注语言信息的电子语料库在自然语言处理和语言学研究中起着至关重要的作用。树库（注有句法信息的语料库）尤其重要，因为它们标记了理解句子和语篇意义所必需的语法结构。对于NLP，树库为开发语言理解系统提供了测试平台。对于语言学研究来说，它们为精确和可复制地研究句法形式的使用模式提供了基础。不幸的是，准确的注释是困难的。自动解析器具有相对较高的错误率，并且人工注释器对这些错误的纠正既缓慢又容易出错。基于Kroch博士和他的合作者在为不同语言创建三个大型树库和质量控制方面取得的最新进展，Kroch博士建议通过创建一个200万单词的英语树库来改善语料库建设。 沿着这一有用和实质性的结果，该项目将发展和测试加速树库建设的假设。这项工作将以两项相辅相成的战略为指导。第一个目的是通过增强部分的语音（POS）标记输入到解析器，而第二个目的是使残留错误的纠正更有效地转移一些负担，从人类到自动错误检测和纠正，以减少解析器的错误率。加快构建准确、一致的树库将提高解析器训练数据的大小和质量，从而提高依赖解析的真实NLP应用程序的性能。更大的树库和更好的构建方法的可用性也将改善语言学研究。此外，随着树库规模的扩大，它们将在文学和历史研究中变得更加有用，在这些研究中，文本的修辞结构将以比目前更精确的方式变得可解释。除了拟议研究的智力价值以及它对依赖自动处理技术的基于文本的研究的影响之外，该项目将为研究生和本科生提供宝贵的培训机会。 在促进语言处理自动化技术的改进方面，该项目也可能有利于工业和政府安全的分析需求。

Electronic corpora annotated with linguistic information play a crucial role in natural language processing (NLP) and in linguistic research. Treebanks (corpora annotated with syntactic information) are especially important since they mark the grammatical structure necessary for understanding sentence and discourse meaning. For NLP, treebanks provide testbeds for developing language understanding systems. For linguistic research, they provide the basis for precise and replicable studies of the patterns of use of syntactic forms. Unfortunately, accurate annotation is difficult. Automatic parsers have relatively high error rates and the correction of these errors by human annotators is both slow and itself error-prone. Based on recent advances that Dr. Kroch and his collaborators have made in the creation and quality control of three large treebanks for different languages, Dr. Kroch proposes a major effort to improve corpus construction through the creation of a two-million-word English treebank. Along with this useful and substantial result, the project will develop and test hypotheses on speeding up treebank construction. The work will be guided by two complementary strategies. The first aims to reduce the parser's error rate by enhancing the part-of-speech (POS) tagged input to the parser while the second aims to make the correction of residual errors more efficient by shifting some of the burden from human to automatic error detection and correction. Speeding up the construction of accurate, consistent treebanks will improve the size and quality of training data for parsers, leading to improved performance in real-world NLP applications that rely on parsing. The availability of larger treebanks and of better methods for constructing them will also improve linguistic research. Moreover, as treebanks grow in size, they will become more useful in literary and historical studies, where the rhetorical structure of texts will become investigable in a more precise way than is currently possible.In addition to the intellectual merit of the proposed research and the impact it can be expected to have on text-based research that relies on automated processing techniques, the project will provide valuable training opportunities for graduate and undergraduate students. In contributing to improvements in automated techniques for language processing, this project may also benefit the analytic needs in industry and government security.