no code implementations • 29 Aug 2023 • Sharath Chandra Guntuku, Thomas Talhelm, Garrick Sherman, Angel Fan, Salvatore Giorgi, Liuqing Wei, Lyle H. Ungar
We used natural language processing to analyze a billion words to study cultural differences on Weibo, one of China's largest social media platforms.
no code implementations • 24 May 2023 • Salvatore Giorgi, Shreya Havaldar, Farhan Ahmed, Zuhaib Akhtar, Shalaka Vaidya, Gary Pan, Lyle H. Ungar, H. Andrew Schwartz, Joao Sedoc
We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens in which conversational agents express a diversity of both states (e. g., emotion) and traits (e. g., personality), just as people do.
1 code implementation • EMNLP (NLP-COVID19) 2020 • Roshan Santosh, H. Andrew Schwartz, Johannes C. Eichstaedt, Lyle H. Ungar, Sharath C. Guntuku
In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving.
1 code implementation • 4 Apr 2019 • Sharath Chandra Guntuku, Mingyang Li, Louis Tay, Lyle H. Ungar
Global acceptance of Emojis suggests a cross-cultural, normative use of Emojis.
no code implementations • 18 Nov 2017 • José Marcio Luna, Eric Eaton, Lyle H. Ungar, Eric Diffenderfer, Shane T. Jensen, Efstathios D. Gennatas, Mateo Wirth, Charles B. Simone II, Timothy D. Solberg, Gilmer Valdes
Additive models, such as produced by gradient boosting, and full interaction models, such as classification and regression trees (CART), are widely used algorithms that have been investigated largely in isolation.
no code implementations • NeurIPS 2011 • Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
Recently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks.
no code implementations • 4 May 2011 • Paramveer S. Dhillon, Dean P. Foster, Sham M. Kakade, Lyle H. Ungar
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace.
no code implementations • NeurIPS 2008 • Ted Sandler, John Blitzer, Partha P. Talukdar, Lyle H. Ungar
Here we present a framework for regularized learning in settings where one has prior knowledge about which features are expected to have similar and dissimilar weights.