CAREER: Robust and Secure Multi-Modal Learning for Library-Scale Text Collections

职业：图书馆规模文本收藏的稳健且安全的多模式学习

• 批准号: 1652536
• 负责人: David Mimno
• 金额: $ 55万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652536&HistoricalAwards=false
• 关键词: CAREER; Robust; Secure; Multi; Modal

The growth of social media and digitized libraries has made computational text analysis a vital tool for modern scholarship. But too often methods that work on standardized collections for expert users don't translate to real-world data analysis. In order to be useful, text mining methodologies need to balance theoretical power with practical application. Real data sets are noisy and complicated. More importantly, vast amounts of data cannot be shared directly due to copyright, including all published books after 1923. This project will develop tools that can be applied to limited, privatized views of documents. Algorithms will focus on reliability and efficiency, so that powerful techniques can be used by non-expert users on easily accessible hardware, such as the 10 million K-12 students using low-powered browser-based Chromebooks thereby increasing the societal impact of the work.Unsupervised text mining methods such as topic models and word embeddings have become popular outside of machine learning because they operate on simple, widely-available representations and identify latent variables that represent recognizable themes, events, or concepts. But standard algorithms do not scale well, require full access to potentially sensitive text collections, and cannot take advantage of non-textual data such as images. Although recent work in spectral inference has produced improvements in speed, current methods are plagued by sensitivity to noisy observations. This work will develop a unified approach to unsupervised text mining based on matrix and tensor factorization. The project will focus on data rectification methods for input matrices, enabling simple algorithms to work dramatically better, even in the presence of sparse and noisy observations, while also reducing model uncertainty. The project will develop new methods for learning from private and sensitive documents by creating public views of non-public data. These will include both noisy representations of individual documents as well as corpus-level summary matrices, and support both strong non-identifiability and weaker non-expressivity criteria. Finally, the project will develop new tools for modeling images and text optimized for the way images actually accompany text in real corpora, rather than short, artificial captions. By jointly modeling large volumes of text and semantically related images, the project will enable users to search for contextually related images, not just visually similar images, and identify topics that are grounded in the visual world, not just in text. For further information see the project web page: http://mimno.infosci.cornell.edu

社交媒体和数字化图书馆的增长使计算文本分析成为现代奖学金的重要工具。但是，对于专家用户的标准化收集的方法，通常不会转化为现实世界数据分析。为了有用，文本挖掘方法需要平衡理论能力与实际应用。真实的数据集嘈杂且复杂。更重要的是，大量数据无法直接由于版权而直接共享，包括1923年之后所有已发表的书籍。该项目将开发可应用于有限的，私有化的文档观点的工具。算法将专注于可靠性和效率，因此非专家用户可以在易于访问的硬件上使用强大的技术，例如使用低功耗的基于浏览器的Chromebooks使用的1000万K-12学生，从而增加工作的社会影响，从而提高了工作的文本挖掘方法，例如，诸如主题模型和诸如Machine of Machine的流行方式，因为他们的流行方式已经流行了，因为它们可以流行，因为它们可以流行，因为他们可以流行，因为它们的流行方式是流行的，因为他们可以流行的机动，并且可以使用。代表可识别主题，事件或概念的变量。但是，标准算法不能很好地扩展，需要完全访问潜在敏感的文本收集，并且无法利用非文本数据（例如图像）。尽管最近的光谱推断研究已经提高了速度，但当前方法对嘈杂观察的敏感性困扰着。这项工作将基于矩阵和张量分解来开发一种统一的方法来无监督的文本挖掘。该项目将重点放在输入矩阵的数据纠正方法上，即使在存在稀疏和嘈杂的观测值的情况下，简单的算法也能够更好地工作，同时还降低了模型的不确定性。该项目将通过创建非公开数据的公众观点来开发从私人和敏感文档学习的新方法。这些将包括单个文档的嘈杂表示以及语料库级的摘要矩阵，并支持强大的非识别性和较弱的非表达标准。最后，该项目将开发用于建模图像和文本的新工具，以优化图像实际上伴随的文本，而不是简短的人工字幕。通过共同建模大量文本和语义相关图像，该项目将使用户能够搜索与上下文相关的图像，而不仅仅是视觉上相似的图像，并确定在视觉世界中基于的主题，而不仅仅是文本中。有关更多信息，请参见项目网页：http：//mimno.infosci.cornell.edu

项目成果

The strange geometry of skip-gram with negative sampling

• DOI: 10.18653/v1/d17-1308
• 发表时间: 2017-09
• 作者: David Mimno;Laure Thompson

Comparing Text Representations: A Theory-Driven Approach

• DOI: 10.18653/v1/2021.emnlp-main.449
• 发表时间: 2021-09
• 期刊: ArXiv
• 作者: Gregory Yauney;David M. Mimno

Combatting The Challenges of Local Privacy for Distributional Semantics with Compression

• 发表时间: 2019
• 作者: Alexandra Schofield

Computational Cut-Ups: The Influence of Dada

计算剪切：达达主义的影响

• 发表时间: 2018
• 期刊: Journal of modern periodical studies
• 影响因子: 0.3
• 作者: Thompson, Laure;Mimno, David
• 通讯作者: Mimno, David

Like Two Pis in a Pod: Author Similarity Across Time in the Ancient Greek Corpus

就像豆荚里的两个 Pi：古希腊语料库中不同时间的作者相似度

• DOI: 10.22148/001c.13680
• 发表时间: 2020
• 期刊: Journal of Cultural Analytics
• 作者: Storey, Grant;Mimno, David
• 通讯作者: Mimno, David