RI-Small: Improving Machine Learning Approaches to Coreference Resolution

RI-Small：改进共指解析的机器学习方法

• 批准号: 0812261
• 负责人: Vincent Ng
• 金额: $ 25万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812261&HistoricalAwards=false
• 关键词: RI; Small; Improving; Machine; Learning

Despite recent successes in machine learning approaches to coreference resolution, the performance of recently developed coreference systems is plateauing. Progress on the coreference task is currently limited in part by the over-reliance on morpho-syntactic knowledge sources, which has led to the induction of overly simplistic coreference heuristics. To bring learning-based coreference resolvers to the next level of performance, this work adopts a knowledge-rich approach, investigating a variety of semantic and discourse knowledge sources for coreference resolution. Specifically, it develops corpus-based methods for inducing semantic features, leveraging publicly available lexical databases and advanced machine-learning and inference techniques. In addition, it broadens the kind of discourse knowledge exploited by existing learning-based coreference systems, generating features for capturing not only the salience of a noun phrase but also the coherence of a text, via the use of discourse segmenters and parsers that are grounded in Centering Theory, Rhetorical Structure Theory, and Grosz and Sidner's discourse theory. The main results of this work will be to demonstrate the benefits of a knowledge-rich approach to learning-based coreference resolution, and to re-introduce the linguistically motivated discourse theories developed in the late 1970s and early 1980s into statistical coreference resolution models. The data sets produced in this research will be made available to the research community, and the experimental results will be disseminated via publications.

尽管最近在机器学习方法的共指消解方面取得了成功，但最近开发的共指系统的性能正在趋于稳定。共指任务的进展目前受到限制，部分原因是过度依赖形态句法知识来源，这导致了过于简单的共指推理。为了使基于学习的共指消解器达到下一个性能水平，这项工作采用了知识丰富的方法，调查了各种语义和语篇知识源的共指消解。具体来说，它开发了基于语料库的方法，用于诱导语义特征，利用公开可用的词汇数据库和先进的机器学习和推理技术。此外，它拓宽了现有的基于学习的共指系统所利用的话语知识，生成的功能，不仅捕捉一个名词短语的显着性，但也是一个文本的连贯性，通过使用话语切分器和解析器，是基于中心理论，修辞结构理论，Grosz和Sidner的话语理论。这项工作的主要成果将是展示一个知识丰富的方法，以学习为基础的共指消解的好处，并重新引入语言动机的话语理论在20世纪70年代末和80年代初到统计共指消解模型。在这项研究中产生的数据集将提供给研究界，实验结果将通过出版物传播。