Automatic Resolution of Semantic Ambiguity in Natural Language

自然语言语义歧义的自动解决

• 批准号: 0092784
• 负责人: Theodore Pedersen
• 金额: $ 34.34万
• 依托单位: University of Minnesota-Twin Cities
• 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: 2001-03-15 至 2007-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0092784&HistoricalAwards=false
• 关键词: Automatic; Resolution; Semantic; Ambiguity; Natural

Most words in natural language have multiple possible meanings. This simple fact causes no end of difficulties for computer systems that seek to understand and generate natural language. The semantic ambiguity of words impacts natural language subtasks such as prepositional phrase attachment and pronoun reference resolution, as well as large-scale applications such as machine translation and information retrieval. Automatic methods that resolve ambiguity in word meaning have the potential to advance the state-of-the-art in natural language processing as a whole, but most approaches to word sense disambiguation have proven difficult to deploy on a wide scale because they are dependent on the availability of specialized sources of knowledge that do not exist across a range of domains. The PI's goal in this project is to develop techniques that will ease and ultimately eliminate knowledge acquisition bottlenecks for word sense disambiguation. He will achieve this by pursuing three specific objectives: 1) develop methods that automatically identify the most relevant contextual features for determining the sense of any ambiguous word; 2) develop disambiguation algorithms that learn from "just a few" manually created examples; and 3) develop unsupervised methods that allow any set of word meanings to serve as the target of the disambiguation process. The combined effect of meeting these objectives will be to liberate word sense disambiguation from dependence on particular knowledge sources and thereby simplify their integration into natural language processing systems

自然语言中的大多数单词都有多种可能的含义。这个简单的事实给试图理解和生成自然语言的计算机系统带来了无穷无尽的困难。词的语义歧义不仅影响介词短语附加、代词指称消解等自然语言子任务，也影响机器翻译、信息检索等大规模应用。解决词义歧义的自动方法有可能提高自然语言处理的整体水平，但事实证明，大多数词义消歧方法很难大规模部署，因为它们依赖于专业知识来源的可用性，而这些知识来源不存在于一系列领域。PI在这个项目中的目标是开发能够缓解并最终消除词义消除歧义的知识获取瓶颈的技术。他将通过追求三个具体目标来实现这一目标：1)开发方法，自动识别最相关的上下文特征，以确定任何歧义单词的含义；2)开发消除歧义的算法，从“几个”手动创建的例子中学习；以及3)开发无监督的方法，允许任何一组单词的含义作为消除歧义过程的目标。达到这些目标的综合效果将是将词义消歧从对特定知识来源的依赖中解放出来，从而简化它们与自然语言处理系统的集成