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  • PaperHighlights
  • 2019
    • 03
    • 02
    • 01
      • Querying Word Embeddings for Similarity and Relatedness
      • Data Distillation: Towards Omni-Supervised Learning
      • A Rank-Based Similarity Metric for Word Embeddings
      • Dict2vec: Learning Word Embeddings using Lexical Dictionaries
      • Graph Convolutional Networks for Text Classification
      • Improving Distributional Similarity with Lessons Learned from Word Embeddings
      • Real-time Personalization using Embeddings for Search Ranking at Airbnb
      • Glyce: Glyph-vectors for Chinese Character Representations
      • Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
      • Distilling the Knowledge in a Neural Network
      • Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrin
      • The (Too Many) Problems of Analogical Reasoning with Word Vectors
      • Linear Ensembles of Word Embedding Models
      • Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
      • Dynamic Meta-Embeddings for Improved Sentence Representations
  • 2018
  • 2017
  • Paper Title as Note Title
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  1. 2019

01

Querying Word Embeddings for Similarity and Relatednesschevron-rightData Distillation: Towards Omni-Supervised Learningchevron-rightA Rank-Based Similarity Metric for Word Embeddingschevron-rightDict2vec: Learning Word Embeddings using Lexical Dictionarieschevron-rightGraph Convolutional Networks for Text Classificationchevron-rightImproving Distributional Similarity with Lessons Learned from Word Embeddingschevron-rightReal-time Personalization using Embeddings for Search Ranking at Airbnbchevron-rightGlyce: Glyph-vectors for Chinese Character Representationschevron-rightAuto-Encoding Dictionary Definitions into Consistent Word Embeddingschevron-rightDistilling the Knowledge in a Neural Networkchevron-rightUncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinchevron-rightThe (Too Many) Problems of Analogical Reasoning with Word Vectorschevron-rightLinear Ensembles of Word Embedding Modelschevron-rightIntrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performancechevron-rightDynamic Meta-Embeddings for Improved Sentence Representationschevron-right
PreviousLanguage Models are Unsupervised Multitask Learnerschevron-leftNextQuerying Word Embeddings for Similarity and Relatednesschevron-right

Last updated 5 years ago