skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Thompson, Laure"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; (, Journal of modern periodical studies)
    Can a tool designed to detect dogs detect Dada? We apply a cutting-edge image analysis tool, convolutional neural networks (CNNs), to a collection of page images from modernist journals. This process radically deforms the images, from cultural artifacts into lists of numbers. We determine whether the system can, nevertheless, distinguish Dada from other, non-Dada avant-garde, and in the process learn something about the cohesiveness of Dada as a visual form. We can also analyze the "mistakes" made in classifying Dada to search for the visual influence of Dada as a movement. 
    more » « less
    Accepted Manuscript Full Text Available
  2. ; (, COLING)
    Most previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the exact opposite is often true. Some users want topic models that highlight differences between, for example, authors, but others seek more subtle connections across authors. We introduce three metrics for identifying topics that are highly correlated with metadata, and demonstrate that this problem affects between 30 and 50% of the topics in models trained on two real-world collections, regardless of the size of the model. We find that we can predict which words cause this phenomenon and that by selectively subsampling these words we dramatically reduce topic-metadata correlation, improve topic stability, and maintain or even improve model quality 
    more » « less
    Accepted Manuscript Full Text Available
  3. ; (, Empirical Methods in Natural Language Processing)
    Despite their ubiquity, word embeddings trained with skip-gram negative sampling (SGNS) remain poorly understood. We find that vector positions are not simply determined by semantic similarity, but rather occupy a narrow cone, diametrically opposed to the context vectors. We show that this geometric concentration depends on the ratio of positive to negative examples, and that it is neither theoretically nor empirically inherent in related embedding algorithms. 
    more » « less
    Accepted Manuscript Full Text Available