Paper - arXiv

Lakshmana, et al (MSR India)


  • CNNs (model by Kim (2014)) do not learn kernels that are semantically coherent (having similar meanings). Propose a method for learning semantically coherent kernels and visualizing them.

Interesting methods

  • A two step approach:
  • Group k-grams into desired number of clusters.
  • Associate a filter with each cluster as a weighted combination for Word2Vec representations of the k-grams that are members of that cluster (learn the weights).
  • Details
    • Clustering: Using concatenations of Word2Vec and SentiwordNet (2 dimentional vectors for positive and negative words) and compute eculidean distances between them for clustering.
    • Study the reusability of learned Kernels


  • Achieve results close to Kim (2014)