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

这项计算研究基础设施计划拨款解决了自动系统执行深度语义处理的两个挑战：识别多义词的上下文合适的含义，以及解释表示事件的短语中动词和名词的含义和相互关系。已采取初步步骤，将现有的四个广泛使用的词汇资源(WordNet、FrameNet、PropBank和VerbNet)与不同但互补的内容和覆盖范围进行调整和联系。探索并测试了完成当前跨资源链接和完全传递闭包的方法。由此产生的基础设施(LexLink)旨在使资源完全可互操作，利用它们在词义消除歧义和语义角色标注方面的独特优势。首先，举办了一次研讨会，自然语言处理和计算语义界的主要代表阐明了对计划资源的需求和要求，并就算法、注释技术和评估提出了建议。第二，词义和语义角色标签(代理、工具等)的跨资源链接的子节对自动传递闭包产生的结果进行评估，产生对错误率的估计，并导致算法的微调。第三，根据人工标注的黄金标准对当前最佳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，用于额外的培训和测试，并改进现有算法。总体而言，这项工作为这一资源提供了坚实的基础，该资源对一系列自然语言应用程序具有显著的有益影响，包括机器翻译、文本摘要和情感分析，影响到医疗保健、营销和教育等领域。

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.