Search Results for author:
Found 7 papers, 2 papers with code
CausalNLP: A Practical Toolkit for Causal Inference with Text
Causal inference is the process of estimating the effect or impact of a treatment on an outcome with other covariates as potential confounders (and mediators) that may need to be controlled.
Sampling Community Structure
We propose a novel method, based on concepts from expander graphs, to sample communities in networks.
ktrain: A Low-Code Library for Augmented Machine Learning
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply.
A Framework for Comparing Groups of Documents
We present a general framework for comparing multiple groups of documents.
Mining Measured Information from Text
We present an approach to extract measured information from text (e. g., a 1370 degrees C melting point, a BMI greater than 29. 9 kg/m^2 ).
Topic Similarity Networks: Visual Analytics for Large Document Sets
We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs.
Exploratory Analysis of Highly Heterogeneous Document Collections
We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections.
