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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 2176–2200 of 4002 papers

TitleStatusHype
Examining Structure of Word Embeddings with PCA—0
Audio Caption in a Car Setting with a Sentence-Level LossCode0
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words—0
Leveraging Pretrained Word Embeddings for Part-of-Speech Tagging of Code Switching Data—0
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble LearningCode0
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions—0
Interpretable Adversarial Training for Text—0
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Learning Multilingual Word Embeddings Using Image-Text Data—0
An Empirical Study on Post-processing Methods for Word Embeddings—0
Sherlock: A Deep Learning Approach to Semantic Data Type DetectionCode0
Debiasing Word Embeddings Improves Multimodal Machine Translation—0
Subspace Detours: Building Transport Plans that are Optimal on Subspace ProjectionsCode0
Self-supervised audio representation learning for mobile devices—0
Fair is Better than Sensational:Man is to Doctor as Woman is to DoctorCode0
GWU NLP Lab at SemEval-2019 Task 3: EmoContext: Effective Contextual Information in Models for Emotion Detection in Sentence-level in a Multigenre Corpus—0
Misspelling Oblivious Word EmbeddingsCode0
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces—0
Deeper Text Understanding for IR with Contextual Neural Language ModelingCode0
Augmenting Data with Mixup for Sentence Classification: An Empirical StudyCode0
Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings—0
Domain adaptation for part-of-speech tagging of noisy user-generated text—0
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselinesCode0
SuperTML: Domain Transfer from Computer Vision to Structured Tabular Data through Two-Dimensional Word Embedding—0
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