Semantic similarity detection is a fundamental task in natural language understanding.
Adding topic information has been useful for previous feature-engineered semantic similarity …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Language models are generally trained on data spanning a wide range of topics (eg, news,
reviews, fiction), but they might be applied to an a priori unknown target distribution (eg …

Recent advances in research tools for the systematic analysis of textual data are enabling
exciting new research throughout the social sciences. For comparative politics, scholars who …

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social
Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how …

In communication research, topic modeling is primarily used for discovering systematic
patterns in monolingual text corpora. To advance the usage, we provide an overview of …

Bayesian (machine) learning has been playing a significant role in machine learning for a
long time due to its particular ability to embrace uncertainty, encode prior knowledge, and …

With the ease of access to information, and its rapid dissemination over the internet (both
velocity and volume), it has become challenging to filter out truthful information from fake …

Transfer learning where the behavior of extracting transferable knowledge from the source
domain (s) and reusing this knowledge to target domain has become a research area of …

Big Data and Social Science: Data Science Methods and Tools for Research and Practice,
Second Edition shows how to apply data science to real-world problems, covering all stages …