BME HLT | Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

June 15, 2017, 8:15
MTA SZTAKI (Lágymányosi u. 11, Budapest) Room 306 or 506

Márton Makrai presents a paper by Gábor Berend (2016 TACL) Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling.

From the abstract:

  • (near) state-of-the art performance for
    • both part-of speech tagging and named entity recognition
    • for a variety of languages
      • reasonable results for more than 40 treebanks for POS tagging,
  • model relies only on a few thousand sparse coding-derived features,
    • without applying any modification of the word representations employed for the different tasks. The proposed model has
    • favorable generalization properties