Training accurate classifiers requires many labels, but each label provides only limited
information (one bit for binary classification). In this work, we propose BabbleLabble, a …

This study explored the use of machine learning to automatically evaluate the accuracy of
students' written explanations of evolutionary change. Performance of the Summarization …

Sentiment analysis is crucial for studying public opinion since it can provide us with valuable
information. Existing sentiment analysis methods rely on finding the sentiment element from …

Asking annotators to explain “why” they labeled an instance yields annotator rationales:
natural language explanations that provide reasons for classifications. In this work, we …

We present an empirical investigation of the annotation cost estimation task for active
learning in a multi-annotator environment. We present our analysis from two perspectives …

In this work, we propose a novel representation of text based on patterns derived from
linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive …

Data programming aims to reduce the cost of curating training data by encoding domain
knowledge as labeling functions over source data. As such it not only requires domain …

Feature space design is a critical part of machine learning. This is an especially difficult
challenge in the field of text classification, where an arbitrary number of features of varying …

We propose graph-of-words as an alternative document representation to the historical bag-
of-words that is extensively used in text mining and retrieval. We represent textual …

Data programming is a programmatic weak supervision approach to efficiently curate large-
scale labeled training data. Writing data programs (labeling functions) requires, however …