Recent advances in text mining have provided new methods for capitalizing on the
voluminous natural language text data created by organizations, their employees, and their …

Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

There is a tendency across different subfields in AI to valorize a small collection of influential
benchmarks. These benchmarks operate as stand-ins for a range of anointed common …

In this paper we show that reporting a single performance score is insufficient to compare
non-deterministic approaches. We demonstrate for common sequence tagging tasks that the …

Data and software are immaterial. Scientists in computer science hence have the unique
chance to let other scientists easily reproduce their findings. Similarly, and with the same …

Background: Research results in artificial intelligence (AI) are criticized for not being
reproducible. Objective: To quantify the state of reproducibility of empirical AI research using …

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 …

In recent years, online social networking has revolutionized interpersonal communication.
The newer research on language analysis in social media has been increasingly focusing …

Rule-based stemmers such as the Porter stemmer are frequently used to preprocess English
corpora for topic modeling. In this work, we train and evaluate topic models on a variety of …

In this article we describe our experiences with computational text analysis involving rich
social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed …