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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 2351–2360 of 4002 papers

TitleStatusHype
ChemBoost: A chemical language based approach for protein-ligand binding affinity predictionCode0
Word-like character n-gram embeddingCode0
Interpreting Word-Level Hidden State Behaviour of Character-Level LSTM Language Models—0
Importance of Self-Attention for Sentiment Analysis—0
Frame- and Entity-Based Knowledge for Common-Sense Argumentative ReasoningCode0
A mostly unlexicalized model for recognizing textual entailment—0
Interpretable Word Embedding Contextualization—0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation—0
Stance Detection in Fake News A Combined Feature Representation—0
Robust Word Vectors: Context-Informed Embeddings for Noisy Texts—0
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