Search Results for author:
Found 7 papers, 2 papers with code
Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings
Focusing on word embeddings, we present a supervised-learning method that, for a given domain (e. g., sports, professions), identifies a subset of model features that strongly improve prediction of human similarity judgments.
How to marry a star: probabilistic constraints for meaning in context
In this paper, we derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual.
High-risk learning: acquiring new word vectors from tiny data
Distributional semantics models are known to struggle with small data.
Projection Al\'eatoire Non-N\'egative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding
Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique.
FOIL it! Find One mismatch between Image and Language caption
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities.
