'Skip gram' presentation slideshows

Ch12. Sequence to sequence, machine translation and text processing

Ch12. Sequence to sequence, machine translation and text processing. KH Wong. Overview. Introduction Part1: Word presentation (word embedding) for text processing Integer mapping BOW (Bag of words) TF-IDF (term frequency-inverse document frequency)

1.03k views • 102 slides

Dependency-Based Word Embeddings

399 views • 39 slides

A Brief Intro To BERT

A Brief Intro To BERT. Qiang Ning Presented at the C3SR weekly meeting 03/05/2019. Roadmap Of Language Modeling. LM. Curse of dimensionality. N-gram. Distributional vectors. Feed-forward neural net ( Bengio , 03). RNN/LSTM ( Mikolov , 10). SVD (LSA: Boulder, 97) (LDA: Berkeley, 03).

1.44k views • 30 slides

Prediction of Information Cascades via Content and Structure Integrated Whole Graph Embedding

BSMDMA Workshop @ IJCAI 2019. Prediction of Information Cascades via Content and Structure Integrated Whole Graph Embedding. Feng Xiaodong ? Zhao Qihang , Liu Zhen Speaker: Feng Xiaodong From: University of Electronic Science and Technology of China. 2019-8-11 Macao, China. CONTENT.

529 views • 29 slides

Word embeddings (continued)

Word embeddings (continued). Idea: learn an embedding from words into vectors Need to have a function W(word) that returns a vector encoding that word. Word embeddings: properties. Relationships between words correspond to difference between vectors.

550 views • 28 slides