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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 1221–1230 of 4002 papers

TitleStatusHype
Ruminating Word Representations with Random Noise Masking—0
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs—0
Topic-aware Contextualized Transformers—0
Key Phrase Extraction & Applause Prediction—0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings—0
Beyond Offline Mapping: Learning Cross Lingual Word Embeddings through Context Anchoring—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Introducing Orthogonal Constraint in Structural ProbesCode0
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings—0
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