80 citations · 311 across the 38 of their papers we have counts for
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Modeling Semantic Relatedness using Global Relation Vectors
Shoaib Jameel, Zied Bouraoui, Steven Schockaert
Word embedding models such as GloVe rely on co-occurrence statistics from a large corpus to learn vector representations of word meaning. These vectors have proven to capture surpr…
Probabilistic Relation Induction in Vector Space Embeddings
Zied Bouraoui, Shoaib Jameel, Steven Schockaert
Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational kn…
Jointly Learning Word Embeddings and Latent Topics
Bei Shi, Wai Lam, Shoaib Jameel +2
Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent t…