CAREER: Reference Resolution for Natural Language Understanding

职业：自然语言理解的参考解决方案

• 批准号: 0233177
• 负责人: Sanda Harabagiu
• 金额: $ 20.65万
• 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: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0233177&HistoricalAwards=false
• 关键词: CAREER; Reference; Resolution; Natural; Language

A major obstacle in building robust systems that extract and interpret information, and summarize and answer questions from texts, is the need to identify the entities referred to by pronouns or other referential expressions. This project extends the PI's prior work involving the development of an empirical reference resolution system that relies on several sets of heuristics that correspond to various forms of reference. In particula, the framework will be extended to learn semantic knowledge that supports consistency checks. This enhancement will provide high precision reference resolution and also enhance substantially the recall of referential links. The research will be evaluated using referenceannotated texts and the Penn Treebank corpora. The outcome will be a corpus-based method for reference resolution for both pronouns and nominal expressions. First, the semantics of all referential noun phrases will be captured. Then, by extending the empirical environment with bootstrapping, this reference resolution technique should lead to a powerful tool capable of resolving reference correctly in a large variety of texts. Finally, the tool will be incorporated both in an information extraction system and in a question/answering system, to measure its contribution to the overall performance of these systems. The proposed research departs from previous approaches to reference resolution, in that it promotes data-driven techniques instead of relying on combinations of linguistic and cognitive aspects of language. The immediate pragmatic outcome indicated by the preliminary results should be a substantial recall enhancement.

建立提取和解释信息以及从文本中总结和回答问题的强大系统的一个主要障碍是需要确定代词或其他指代表达所指的实体。该项目扩展了PI之前的工作，包括开发一个经验参照解析系统，该系统依赖于与各种形式的参照相对应的几套启发式规则。具体地说，该框架将被扩展到学习支持一致性检查的语义知识。这一改进将提供高精度的参考分辨率，并大大提高参考链接的召回率。这项研究将使用参考注释文本和宾夕法尼亚大学树库语料库进行评估。其结果将是一个基于语料库的方法，用于代词和名词性表达的指称解析。首先，将捕获所有指代名词短语的语义。然后，通过用Bootstrapping扩展经验环境，这种指称解析技术应该会导致一个强大的工具，能够在大量不同的文本中正确地解析指称。最后，该工具将纳入一个信息提取系统和一个问题/回答系统，以衡量其对这些系统整体业绩的贡献。这项研究不同于以往的指称解决方法，因为它促进了数据驱动的技术，而不是依赖于语言的语言和认知方面的组合。初步结果表明，直接的务实结果应该是大幅提高记忆力。