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Found 10 papers, 4 papers with code
Popularity Prediction of Online Petitions using a Multimodal DeepRegression Model
Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause.
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
Fairness-aware Class Imbalanced Learning
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups.
Evaluating Debiasing Techniques for Intersectional Biases
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques.
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies
More than 200 generic drugs approved by the U. S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer.
Deep Ordinal Regression for Pledge Specificity Prediction
Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability.
Target Based Speech Act Classification in Political Campaign Text
We study pragmatics in political campaign text, through analysis of speech acts and the target of each utterance.
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy.
Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis
Election manifestos document the intentions, motives, and views of political parties.
