Collaborative: Improving Subjectivity Analysis to Achieve High-Precision Information Extraction

本项目将使用主观性分析来提高信息提取(IE)系统的准确性。IE系统旨在提取事实，但它们容易受到指控、指控、怀疑和意见等主观陈述的错误命中。研究的第一阶段将创建一个主观性分类器，它使用学习算法来识别与主观语言相关的语言特征。量词将使用几种自然语言表示，包括提取模式、N-gram和名词短语。分类器将嵌入引导体系结构中，以便它可以从未注释的语料库中学习，只需要少量的注释数据就可以启动引导。在第二阶段，分类器将被集成到IE系统中，以衡量主观分类对IE性能的影响。从客观句中提取的信息将被视为事实，而从主观句中提取的信息将被标记为不确定或被丢弃。这项研究将使我们更好地理解主观语言是如何表达的，以及语境在认识主体性方面所起的作用。这项研究的潜在影响是产生更准确的主观性分类器，并证明主观性分析可以提高IE系统的性能。

This project will use subjectivity analysis to improve the accuracy of information extraction (IE) systems. IE systems are designed to extract facts, but they are prone to false hits from subjective statements such as accusations, allegations, suspicions, and opinions. The first phase of the research will create a subjectivity classifier that uses learning algorithms to identify linguistic features associated with subjective language. The classifier will use several natural language representations, including extraction patterns, N-grams, and noun phrases. The classifier will be embedded in a bootstrapping architecture so that it can learn from unannotated corpora, requiring only a small amount of annotated data to jumpstart the bootstrapping. In the second phase, the classifier will be integrated into an IE system to measure the impact of subjectivity classification on IE performance. Information extracted from objective sentences will be treated as facts, but information extracted from subjective sentences will be labeled as uncertain or discarded. This research will produce a better understanding of how subjective language is expressed and the role that context plays in recognizing subjectivity. The potential impact of the research is to produce more accurate subjectivity classifiers and to demonstrate that subjectivity analysis can improve the performance of IE systems.