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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 2151–2175 of 4002 papers

TitleStatusHype
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words—0
Audio Caption in a Car Setting with a Sentence-Level LossCode0
Examining Structure of Word Embeddings with PCA—0
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions—0
Interpretable Adversarial Training for Text—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks—0
Adapting Text Embeddings for Causal InferenceCode1
Learning Multilingual Word Embeddings Using Image-Text Data—0
Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic FormulaeCode1
An Empirical Study on Post-processing Methods for Word Embeddings—0
Sherlock: A Deep Learning Approach to Semantic Data Type DetectionCode0
Self-supervised audio representation learning for mobile devices—0
Debiasing Word Embeddings Improves Multimodal Machine Translation—0
Subspace Detours: Building Transport Plans that are Optimal on Subspace ProjectionsCode0
Fair is Better than Sensational:Man is to Doctor as Woman is to DoctorCode0
Misspelling Oblivious Word EmbeddingsCode0
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
Action Assembly: Sparse Imitation Learning for Text Based Games with Combinatorial Action Spaces—0
Augmenting Data with Mixup for Sentence Classification: An Empirical StudyCode0
Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings—0
Deeper Text Understanding for IR with Contextual Neural Language ModelingCode0
Domain adaptation for part-of-speech tagging of noisy user-generated text—0
SuperTML: Domain Transfer from Computer Vision to Structured Tabular Data through Two-Dimensional Word Embedding—0
Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselinesCode0
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