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: 9983600
• 负责人: Sanda Harabagiu
• 金额: $ 30万
• 依托单位: Southern Methodist University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-03-01 至 2001-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9983600&HistoricalAwards=false
• 关键词: CAREER; Reference; Resolution; Natural; Language

This is the first year of funding of a 4-year continuing award. A major obstacle in building robust systemsthat 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 particular, 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.

这是一个为期4年的连续奖励的第一年。在建立强大的系统，提取和解释信息，并总结和回答问题的文本中，一个主要的障碍是需要确定的实体所指的代词或其他指称表达。该项目扩展了PI先前的工作，涉及开发一个经验参考解析系统，该系统依赖于与各种形式的参考对应的几组几何学。特别是，该框架将扩展到学习支持一致性检查的语义知识。这种增强将提供高精度的参考分辨率，也大大提高了引用链接的召回。本研究将使用参考注释文本和宾州树库语料库进行评估。结果将是一个基于语料库的代词和名词性表达的参考决议的方法。首先，将捕获所有指称名词短语的语义。然后，通过扩展的经验环境与引导，这种参考决议技术应导致一个强大的工具，能够正确地解决在各种各样的文本参考。最后，该工具将被纳入信息提取系统和问答系统，以衡量其对这些系统的整体性能的贡献。拟议的研究偏离了以前的参考解析方法，因为它促进了数据驱动的技术，而不是依赖于语言和认知方面的组合。初步结果表明，直接的实用结果应该是一个实质性的回忆增强。