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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 2026–2050 of 4002 papers

TitleStatusHype
Quantifying Context Overlap for Training Word Embeddings—0
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs—0
Quantum Algorithms for Compositional Text Processing—0
Quantum-inspired Complex Word Embedding—0
Query Expansion for Cross-Language Question Re-Ranking—0
Query Expansion with Locally-Trained Word Embeddings—0
Querying Word Embeddings for Similarity and Relatedness—0
Query Obfuscation by Semantic Decomposition—0
Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters—0
Question Answering over Freebase with Multi-Column Convolutional Neural Networks—0
Questioning Arbitrariness in Language: a Data-Driven Study of Conventional Iconicity—0
Question Type Classification Methods Comparison—0
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition—0
Racial Bias Trends in the Text of US Legal Opinions—0
Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection—0
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction—0
Random Walks and Neural Network Language Models on Knowledge Bases—0
Ranking Convolutional Recurrent Neural Networks for Purchase Stage Identification on Imbalanced Twitter Data—0
Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model—0
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement—0
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings—0
RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English)—0
Reading Between the Lines: Overcoming Data Sparsity for Accurate Classification of Lexical Relationships—0
Realised Volatility Forecasting: Machine Learning via Financial Word Embedding—0
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation—0
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