430 citations · 1.5k across the 43 of their papers we have counts for
8 papers · 1 filter
Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events
Ian Stewart, Diyi Yang, Jacob Eisenstein
Social media datasets make it possible to rapidly quantify collective attention to emerging topics and breaking news, such as crisis events. Collective attention is typically measu…
Follow the Leader: Documents on the Leading Edge of Semantic Change Get More Citations
Sandeep Soni, Kristina Lerman, Jacob Eisenstein
Diachronic word embeddings -- vector representations of words over time -- offer remarkable insights into the evolution of language and provide a tool for quantifying sociocultural…
How we do things with words: Analyzing text as social and cultural data
Dong Nguyen, Maria Liakata, Simon DeDeo +4
In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the…
Clinical Concept Extraction for Document-Level Coding
Sarah Wiegreffe, Edward Choi, Sherry Yan +2
The text of clinical notes can be a valuable source of patient information and clinical assessments. Historically, the primary approach for exploiting clinical notes has been infor…
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
Xiaochuang Han, Jacob Eisenstein
Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large co…
Character Eyes: Seeing Language through Character-Level Taggers
Yuval Pinter, Marc Marone, Jacob Eisenstein
Character-level models have been used extensively in recent years in NLP tasks as both supplements and replacements for closed-vocabulary token-level word representations. In one p…