61 citations · 115 across the 19 of their papers we have counts for
5 papers · 2 filters
Unsupervised Label Refinement Improves Dataless Text Classification
Zewei Chu, Karl Stratos, Kevin Gimpel
Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising…
Corrected CBOW Performs as well as Skip-gram
Ozan İrsoy, Adrian Benton, Karl Stratos
Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent…
Mining Knowledge for Natural Language Inference from Wikipedia Categories
Mingda Chen, Zewei Chu, Karl Stratos +1
Accurate lexical entailment (LE) and natural language inference (NLI) often require large quantities of costly annotations. To alleviate the need for labeled data, we introduce Wik…
NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources
Zewei Chu, Karl Stratos, Kevin Gimpel
We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit. NatCat consists of document-category…
Discrete Latent Variable Representations for Low-Resource Text Classification
Shuning Jin, Sam Wiseman, Karl Stratos +1
While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically…