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

社交媒体和数字化图书馆的发展使计算文本分析成为现代学术的重要工具。但太多时候，为专家用户提供标准化收集的方法不能转化为现实世界的数据分析。为了有用，文本挖掘方法需要在理论力量和实际应用之间取得平衡。真实的数据集是嘈杂和复杂的。更重要的是，由于版权的原因，海量数据无法直接共享，包括1923年以后出版的所有书籍。该项目将开发可应用于有限的、私有化的文件查看的工具。算法将关注可靠性和效率，以便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1000万名K-12学生使用基于低性能浏览器的Chromebook，从而增加工作的社会影响。无监督文本挖掘方法，如主题模型和单词嵌入，已成为机器学习之外的流行方法，因为它们操作简单、广泛可用的表示，并识别代表可识别的主题、事件或概念的潜在变量。但标准算法不能很好地扩展，需要完全访问潜在敏感的文本集合，并且不能利用图像等非文本数据。虽然最近在光谱推断方面的工作在速度上有所改进，但当前的方法受到对噪声观测的敏感性的困扰。这项工作将开发一种基于矩阵和张量分解的无监督文本挖掘的统一方法。该项目将专注于输入矩阵的数据校正方法，使简单的算法即使在存在稀疏和噪声观测的情况下也能显著更好地工作，同时还减少了模型的不确定性。该项目将通过创建非公共数据的公共视图，开发从私人和敏感文件中学习的新方法。这些将包括单个文件的噪声表示以及语料库级别的摘要矩阵，并支持强不可识别性和弱不可表现性标准。最后，该项目将开发新的工具来对图像和文本进行建模，以优化图像在真实语料库中实际伴随文本的方式，而不是简短的人造字幕。通过对大量文本和语义相关图像进行联合建模，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，并识别基于视觉世界的主题，而不仅仅是文本。有关更多信息，请参阅项目网页：http://mimno.infosci.cornell.edu

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