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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 2001–2050 of 4002 papers

TitleStatusHype
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge—0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition—0
Probabilistic Analogical Mapping with Semantic Relation Networks—0
Probabilistic Bias Mitigation in Word Embeddings—0
Probabilistic Embeddings with Laplacian Graph Priors—0
Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation—0
Probabilistic Relation Induction in Vector Space Embeddings—0
Probing Brain Context-Sensitivity with Masked-Attention Generation—0
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?—0
Probing the statistical properties of enriched co-occurrence networks—0
Probing Word and Sentence Embeddings for Long-distance Dependencies Effects in French and English—0
Problems With Evaluation of Word Embeddings Using Word Similarity Tasks—0
Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking—0
Pruning Basic Elements for Better Automatic Evaluation of Summaries—0
PSDVec: a Toolbox for Incremental and Scalable Word Embedding—0
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings—0
Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?—0
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings—0
PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection—0
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings—0
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base—0
QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian—0
Quality of Word Embeddings on Sentiment Analysis Tasks—0
Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings—0
Quantifying and Reducing Stereotypes in Word Embeddings—0
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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