CI-P: Collaborative Research: LexLink: Aligning WordNet, FrameNet, PropBank and VerbNet

CI-P：协作研究：LexLink：对齐 WordNet、FrameNet、PropBank 和 VerbNet

• 批准号: 1205484
• 负责人: Martha Palmer
• 金额: $ 2.5万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205484&HistoricalAwards=false
• 关键词: CI; Collaborative; Research; LexLink; Aligning

This Computing Research Infrastructure planning grant addresses two challenges for automatic systems performing deep semantic processing: identifying the context-appropriate sense of polysemous words and interpreting the meanings and interrelations of verbs and nouns in event-denoting phrases. Preliminary steps are taken for aligning and linking four existing widely used lexical resources (WordNet, FrameNet, PropBank and VerbNet) with different but complementary contents and coverage. Methods for completing current cross-resource links and full transitive closure are explored and tested. The resulting infrastructure (LexLink) is designed to make the resources fully interoperable, capitalizing on their particular strengths with respect to word sense disambiguation and Semantic Role labeling.Four activities are carried out in the context of planning LexLink. First, a workshop is held where key representatives of the Natural Language Processing and computational semantics communities articulate needs and requirements for the planned resource and offer advice on algorithms, annotation techniques and evaluation. Second, a subsection of cross-resource links for word senses and Semantic Role labels (Agent, Instrument, etc.) resulting from the automatic transitive closure is evaluated, yielding estimates for the error rate and leading to fine-tuning of algorithms. Third, current best performing mapping algorithms for word senses and Semantic Role labels are evaluated against a human-annotated Gold Standard. Fourth, new Gold Standard data are created for additional training and testing and to refine existing algorithms. As a whole, the work provides a solid foundation for a resource with significant beneficial impact on a range of natural language applications, including machine translation, text summarization and sentiment analysis affecting areas such as health care, marketing, and education.

这项计算研究基础设施计划拨款解决了执行深度语义处理的自动系统面临的两个挑战：识别多义词的上下文适当意义，解释事件表示短语中动词和名词的意义和相互关系。对现有的四个广泛使用的词汇资源（WordNet, FrameNet， PropBank和vernet）进行了初步的调整和连接，这些资源的内容和覆盖范围不同但又互补。完成当前跨资源链接和完全传递闭包的方法进行了探索和测试。由此产生的基础设施（LexLink）旨在使资源完全可互操作，利用它们在词义消歧和语义角色标记方面的特殊优势。在规划LexLink的背景下开展了四项活动。首先，举办了一个研讨会，自然语言处理和计算语义社区的主要代表阐明了计划资源的需求和要求，并就算法、注释技术和评估提供了建议。其次，评估由自动传递闭包产生的用于词意和语义角色标签（Agent， Instrument等）的跨资源链接的一部分，得出错误率的估计并导致算法的微调。第三，根据人工注释的黄金标准对当前表现最好的词义和语义角色标签映射算法进行评估。第四，创建新的黄金标准数据用于额外的训练和测试，并改进现有的算法。总的来说，这项工作为一个对一系列自然语言应用产生重大有益影响的资源提供了坚实的基础，包括机器翻译、文本摘要和影响医疗保健、市场营销和教育等领域的情感分析。