Probabilistic topic models like Latent Dirichlet Allocation (LDA) have b...
Natural Language Search (NLS) extends the capabilities of search engines...
Word embeddings are high dimensional vector representations of words tha...
Automatically predicting the level of non-native English speakers given ...
Discovering whether words are semantically related and identifying the
s...
Many information retrieval algorithms rely on the notion of a good dista...
The paper describes the CAp 2017 challenge. The challenge concerns the
p...
Traditional sentiment analysis approaches tackle problems like ternary
(...
Word clusters have been empirically shown to offer important performance...
We investigate the integration of word embeddings as classification feat...
This paper describes the participation of the team "TwiSE" in the SemEva...
This report describes our participation in the cDiscount 2015 challenge ...
Probabilistic topic models are generative models that describe the conte...