3.2k citations · 5.4k across the 17 of their papers we have counts for
18 papers
Natural Language Decompositions of Implicit Content Enable Better Text Representations
Alexander Hoyle, Rupak Sarkar, Pranav Goel +1
When people interpret text, they rely on inferences that go beyond the observed language itself. Inspired by this observation, we introduce a method for the analysis of text that t…
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
Carlos Aguirre, Mark Dredze, Philip Resnik
Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms acr…
Are Neural Topic Models Broken?
Alexander Hoyle, Pranav Goel, Rupak Sarkar +1
Recently, the relationship between automated and human evaluation of topic models has been called into question. Method developers have staked the efficacy of new topic model varia…
Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence
Alexander Hoyle, Pranav Goel, Denis Peskov +3
Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rel…
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani +4
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the tas…
Improving Neural Topic Models using Knowledge Distillation
Alexander Hoyle, Pranav Goel, Philip Resnik
Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of…