Word embeddings are becoming popular for political science research, yet we know little
about their properties and performance. To help scholars seeking to use these techniques …

Word embeddings are a powerful machine-learning framework that represents each English
word by a vector. The geometric relationship between these vectors captures meaningful …

This monograph aims at providing an introduction to key concepts, algorithms, and
theoretical results in machine learning. The treatment concentrates on probabilistic models …

Social scientists commonly seek to make statements about how word use varies over
circumstances—including time, partisan identity, or some other document-level covariate …

The problem of comparing two bodies of text and searching for words that differ in their
usage between them arises often in digital humanities and computational social science …

Modern learning algorithms excel at producing accurate but complex models of the data.
However, deploying such models in the real-world requires extra care: we must ensure their …

Contextual situations, such as having dinner at a restaurant on Friday with the spouse,
became a useful mechanism to represent context in context-aware recommender systems …

Word embedding, which represents individual words with semantically fixed-length vectors,
has made it possible to successfully apply deep learning to natural language processing …

Since the beginning of this millennium, data in the form of human-generated text in a
machine-readable format has become increasingly available to social scientists, presenting …

Many questions of legal interpretation hinge on whether two groups of people assign
different meanings to the same word. For example: Do 18th-and 21st-century English …