Search Results for author:
Found 10 papers, 8 papers with code
TopicGPT: A Prompt-based Topic Modeling Framework
Topic modeling is a well-established technique for exploring text corpora.
Natural Language Decompositions of Implicit Content Enable Better Text Representations
When people interpret text, they rely on inferences that go beyond the observed language itself.
Revisiting Automated Topic Model Evaluation with Large Language Models
Topic models are used to make sense of large text collections.
Are Neural Topic Models Broken?
Recently, the relationship between automated and human evaluation of topic models has been called into question.
Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence
To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.
Promoting Graph Awareness in Linearized Graph-to-Text Generation
Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graph-encoding neural networks.
Improving Neural Topic Models using Knowledge Distillation
Topic models are often used to identify human-interpretable topics to help make sense of large document collections.
Unsupervised Discovery of Gendered Language through Latent-Variable Modeling
Studying the ways in which language is gendered has long been an area of interest in sociolinguistics.
Combining Sentiment Lexica with a Multi-View Variational Autoencoder
When assigning quantitative labels to a dataset, different methodologies may rely on different scales.
