We propose Rotom, a multi-purposed data augmentation framework for training high-quality machine learning models while requiring only a small number (e.g., 200) of labeled examples.
Snippext is a state-of-the-art (SOTA) opinion mining pipeline that extracts aspects, opinions, and sentiments from user-generated content such as online reviews. It allows for a reduction of 50% or more of the training data usually required.
ExtremeReader generates both a structured and abstractive summarization that are easier to interpret. It also allows users to explore and see explanations of these summaries by drilling down or up to the desired level of granularity. Users can even see the sentence from which the opinion features were extracted.
We built HappyDB, a crowd-sourced collection of 100,000 happy moments that we make publicly available. Our goal is to build NLP technology that understands how people express their happiness in text while achieving insights into happiness-leading events and scenarios on a scale.
We developed OpineDB a subjective database system that addresses these challenges by interpreting subjective predicates against a database schema through a combination of natural language processing (NLP) and information retrieval (IR) techniques.
