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）。 描述具有相同“角色”集合的相同类型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13,687个单词定义了超过1,224个框架，并标记了其中的202,978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研究人员就可以在未来几年内阅读和下载数据。FrameNet网站是成千上万访问者进入Frame Semantics的重要入口。无论是普通访问者还是研究者，都可以通过搜索词汇单位来找到框架，或者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的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。例如，最近的研究创造性地将统计和符号方法结合到NLU中，例如使用BERT（Devlin et al. 2019）来改进自动语义角色标记，或将FrameNet标签注入神经网络。FrameNet的影响比最初设想的要广泛得多。 教师使用FrameNet向各级学生介绍词汇语义。数以千计的FrameNet数据下载引发了传统NLP之外的各种研究，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FrameNet在十几种语言中的发展，以及对翻译时框架和框架转换的跨语言投影的研究。Berkeley FrameNet项目也雇佣了许多来自不同背景的学生，他们中的许多人已经在NLP和/或学术界取得了成功的职业生涯。该项目的URL是https://framenet.icsi.bekeley.edu由于该项目以前的服务器正在老化，很快将退役，该URL已被重定向到巴西联邦Juiz de Fora大学服务器上的FrameNet公共网站的副本，多亏了那里同事的帮助。 该项目的目的是在美国的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，将无限期地保存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知识价值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.