Berkeley FrameNet Website Migration

FrameNet团队已经建立了一个超级英语词典，人类可以阅读，计算机也可以用于自然语言处理(NLP)。用相同的“角色”集合来描述同一类事件的词被组合在一个语义框架中。在过去的25年里，FrameNet的研究人员已经为13687个单词定义了1224个框架，并标记了202978个例子。该项目将把FrameNet公共网站从几个老化的服务器转移到云托管，这样学生和NLP研究人员在未来几年都可以阅读和下载数据。FrameNet网站是成千上万访问者了解框架语义的重要切入点。不经意的来访者和研究者都可以通过搜索词汇单位来找到框架，也可以通过搜索框架来找到词汇单位。计算语言学的研究得益于Fillmore框架语义学理论的影响，FrameNet数据被广泛应用于自然语言理解系统的开发中。例如，最近的研究创造性地结合了统计和符号方法来处理NLU，例如使用Bert(Devlin等人。2019年)改进自动语义角色标记，或将FrameNet标签注入神经网络。FrameNet产生了比最初预期更广泛的影响。教师使用FrameNet向所有级别的学生介绍词汇语义。数以千计的FrameNet数据下载引发了传统NLP以外的各种研究，例如检测推文中的道德价值观，探索宗教和政治文本中的框架。Berkeley FrameNet启发了十几种语言的FrameNet的开发，以及对翻译时框架和框架转换的跨语言投影的研究。伯克利FrameNet项目还雇佣了许多来自不同背景的学生，其中许多人在自然语言规划和/或学术界取得了成功。该项目的URL是https://framenet.icsi.bekeley.edu。由于该项目之前的服务器已经老化，即将退役，该URL已被重定向到巴西华伊士联邦大学服务器上的FrameNet公共网站的副本，这要归功于那里同事的帮助。这个项目的目的是在美国的一个云托管平台上重建网站和数据库，并在那里维护至少五年。FrameNet词汇数据库，包括所有框架、框架元素、词汇单元和注释，将无限期保留在世界各地的安全地点。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The FrameNet team has built a super-dictionary of English that humans can read and computers can also use for natural language processing (NLP). Words that describe the same kind of event with the same set of "roles" are grouped together in a SEMANTIC FRAME. Over the past 25 years, FrameNet researchers have defined more than 1,224 frames for 13,687 words and labeled 202,978 examples of them. This project will shift the FrameNet public website from several aging servers to cloud hosting, so that students and NLP researchers will be able to read and download the data for years to come.The FrameNet website is a vital point of entry into Frame Semantics for thousands of visitors. Both casual visitors and researchers can either search for lexical units to find frames, or search frames to find lexical units. Computational Linguistics research has benefited from the influence of the theory of Fillmore's Frame Semantics, and FrameNet data has been widely used in the development of systems for NLU. For example, recent research has creatively combined statistical and symbolic approaches to NLU, such as using BERT (Devlin et al. 2019) to improve automatic semantic role labeling, or injecting FrameNet labels into neural nets.FrameNet has had much broader impact than initially envisioned. Instructors use FrameNet to introduce students at all levels to lexical semantics. The thousands of downloads of FrameNet data have sparked various studies outside of traditional NLP, e.g. detection of moral values in tweets, explorations of framing in religious and political texts. Berkeley FrameNet has inspired development of FrameNets in a dozen languages and research on cross-lingual projections of frames and frame shifts when translating. The Berkeley FrameNet project has also hired many students from diverse backgrounds, many of whom have gone on to successful careers in NLP and/or academia.The URL of the project is https://framenet.icsi.bekeley.edu Because the previous servers for the project are aging and will be retired soon, this URL has been redirected to a duplicate of the FrameNet public website on a server at Universidade Federal de Juiz de Fora in Brazil, thanks to help from colleagues there. The purpose of this project is to recreate the website and database on a cloud-hosting platform in the US, and maintain it there for at least five years. The FrameNet lexical database, including all frames, frame elements, lexical units, and annotations will be retained indefinitely at secure locations worldwide.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.