ITR: Framenet++: An On-Line Lexical Semantic Resource and its Application to Speech and Language Understanding

ITR：框架网：在线词汇语义资源及其在语音和语言理解中的应用

• 批准号: 0086132
• 负责人: Charles Fillmore
• 金额: $ 209.84万
• 依托单位: International Computer Science Institute
• 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: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0086132&HistoricalAwards=false
• 关键词: ITR; Framenet++; Line; Lexical; Semantic

This is the first year funding of a three-year continuing award. Robust domain-independent language understanding is essential for multilingual information extraction, summarization, question answering, and automatic translation. With pervasive computing environments soon to come, language understanding will become even more indispensable for interacting with artifacts of widely different functionalities. The field of natural language understanding has made significant progress in the last fifteen years. A large part of this gain is due to the sophisticated combination of statistical algorithms with template-based algorithms tailored to specific domains like air-traffic information, travel scheduling, and business news. But any real solution to the problem of domain-independent understanding will require moving beyond template-based monolingual systems to more flexible, general purpose HCI systems via three key innovations: (1) a domain-independent semantic language as the back end for these understanding systems, replacing the current domain-restricted templates and slots; (2) rich semantic lexical databases which are broad enough to cover the necessary words for language engineering tasks, and deep enough in usable semantic information to support true domain-independent understanding; and (3) sophisticated techniques for performing this mapping.This project will develop these three components: a very large lexical database FrameNet++, a semantic language designed for domain-independent understanding tasks, and the tools for applying it to and evaluating it on key NLU applications. The semantic language and lexical database are based on formalizing the semantic frames and the semantic and syntactic combinatory properties - the valences - of a significant portion of the English lexicon. FrameNet++ will offer significantly richer semantic information than is available in current databases like COMLEX and WordNet, by characterizing the conceptual frames within which words are defined and identifying the semantic roles which the arguments of these words can take. These roles and frames are key to building domain-independent language understanding applications. The project will focus from the start on specific NLU applications: word sense disambiguation, information extraction, multilingual information extraction, and an eventual extension to text data mining. For each application, the PI and his team will apply the FrameNet++ system to improve the domain independence of the semantic components, using statistical algorithms for semantic annotation that we have already begun to implement. These applications will in turn provide a rich and realistic evaluation framework to guide FrameNet++ development, and will encourage potential users to apply it to a wide variety of tasks.The FrameNet++ database will be capable of serving many purposes. Provided with statistical information about frequencies of words, word/sense mappings, and combinatorial patterns linked to word senses, it will be usable in various automatic language understanding processes, including word sense disambiguation and information extraction. Since the formal semantic annotations are keyed to conceptual structures which are independent of any individual language, they are available for the creation of parallel lexicon databases of other languages. The semantic structures in the databases will facilitate matches from one language to another, in machine translation and machine-assisted translation, while the syntactic structures allow the production of appropriate grammatical sentences in the target language.

这是一个为期三年的连续奖励的第一年资助。 鲁棒的领域无关语言理解对于多语言信息提取、摘要、问答和自动翻译至关重要。 随着普适计算环境的即将到来，语言理解将变得更加不可或缺的与广泛不同功能的工件进行交互。 自然语言理解领域在过去15年中取得了重大进展。 这一收益的很大一部分是由于统计算法与基于模板的算法的复杂组合，这些算法针对特定领域，如空中交通信息，旅行计划和商业新闻。 但是，任何真实的解决领域独立理解问题的方案都需要超越基于模板的单语系统，通过三个关键的创新来实现更灵活、更通用的人机交互系统： (1)一种独立于领域的语义语言作为这些理解系统的后端，取代目前受领域限制的模板和插槽; (2)丰富的语义词汇数据库，其范围足以涵盖语言工程任务所需的词汇，并且具有足够的可用语义信息，以支持真正的独立于领域的理解; 以及（3）执行此映射的复杂技术。此项目将开发以下三个组件： 一个非常大的词汇数据库FrameNet++，一种为独立于领域的理解任务设计的语义语言，以及将其应用于关键NLU应用程序并对其进行评估的工具。 语义语言和词汇数据库是基于形式化的语义框架和语义和句法的组合属性-配价-的一个重要部分的英语词汇。 FrameNet++将提供比目前的数据库（如COMLEX和WordNet）更丰富的语义信息，通过描述定义单词的概念框架，并确定这些单词的参数可以承担的语义角色。 这些角色和框架是构建独立于领域的语言理解应用程序的关键。 该项目将从一开始就专注于特定的NLU应用： 词义消歧、信息抽取、多语言信息抽取以及最终扩展到文本数据挖掘。 对于每个应用程序，PI和他的团队将应用FrameNet++系统来提高语义组件的域独立性，使用我们已经开始实施的语义注释统计算法。 这些应用程序将反过来提供一个丰富和现实的评估框架，以指导FrameNet++的开发，并将鼓励潜在用户将其应用于各种各样的任务。FrameNet++数据库将能够服务于许多目的。 提供的统计信息的频率的话，词/意义映射，和组合模式链接到词义，它将是可用的各种自动语言理解过程中，包括词义消歧和信息提取。 由于形式语义标注的关键是概念结构，这是独立于任何个别的语言，他们可用于创建其他语言的并行词典数据库。 在机器翻译和机器辅助翻译中，数据库中的语义结构将有助于从一种语言到另一种语言的匹配，而句法结构则允许在目标语言中产生适当的语法句子。