Sign languages are used as a primary language by approximately 70 million
D/deaf people world-wide. However, most communication technologies operate in
spoken and written languages, creating inequities in access. To help tackle
this problem, we release ASL Citizen, the largest Isolated Sign Language
Recognition (ISLR) dataset to date, collected with consent and containing
83,912 videos for 2,731 distinct signs filmed by 52 signers in a variety of
environments. We propose that this dataset be used for sign language dictionary
retrieval for American Sign Language (ASL), where a user demonstrates a sign to
their own webcam with the aim of retrieving matching signs from a dictionary.
We show that training supervised machine learning classifiers with our dataset
greatly advances the state-of-the-art on metrics relevant for dictionary
retrieval, achieving, for instance, 62% accuracy and a recall-at-10 of 90%,
evaluated entirely on videos of users who are not present in the training or
validation sets. An accessible PDF of this article is available at
this https URL

发布了迄今最大的孤立手语识别数据集 ASL Citizen，该数据集包含 83,912 个视频，由 52 名手语用户在各种环境中拍摄了 2,731 个不同的手势，并提出应将该数据集用于手语字典检索，使用我们数据集训练监督学习分类器在手语字典检索方面极大地提高了性能。

ASL Citizen: 一个社区资源数据集，推进独立手势识别

ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign  Language Recognition

Methods and applications are inextricably linked in science, and in
particular in the domain of text-as-data. In this paper, we examine one such
text-as-data application, an established economic index that measures economic
policy uncertainty from keyword occurrences in news. This index, which is shown
to correlate with firm investment, employment, and excess market returns, has
had substantive impact in both the private sector and academia. Yet, as we
revisit and extend the original authors' annotations and text measurements we
find interesting text-as-data methodological research questions: (1) Are
annotator disagreements a reflection of ambiguity in language? (2) Do
alternative text measurements correlate with one another and with measures of
external predictive validity? We find for this application (1) some annotator
disagreements of economic policy uncertainty can be attributed to ambiguity in
language, and (2) switching measurements from keyword-matching to supervised
machine learning classifiers results in low correlation, a concerning
implication for the validity of the index.

通过分析一个测量经济政策不确定性的经济学指数，探讨了文本 - 数据应用中的研究方法和应用之间的联系，并发现了对文本 - 数据方法论的研究问题。研究结果表明，(1) 作为关于经济政策不确定性的注释者的分歧可以归因于语言的模糊性，(2) 从匹配关键字的方法转向监督式机器学习分类器的方法将导致低相关性，这对指数的有效性有潜在影响。