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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 1476–1500 of 4002 papers

TitleStatusHype
Embeddings as representation for symbolic music—0
Contextual Embeddings: When Are They Worth It?—0
Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddingsCode0
Text Classification with Few Examples using Controlled Generalization—0
Adversarial Training for Commonsense InferenceCode1
Cross-Lingual Word Embeddings for Turkic LanguagesCode0
RPD: A Distance Function Between Word Embeddings—0
Towards classification parity across cohorts—0
Learning Probabilistic Sentence Representations from Paraphrases—0
Cross-lingual Transfer of Sentiment Classifiers—0
Pretraining Federated Text Models for Next Word PredictionCode1
Evaluating Sparse Interpretable Word Embeddings for Biomedical DomainCode0
Toward Better Storylines with Sentence-Level Language Models—0
Article citation study: Context enhanced citation sentiment detection—0
Comparative Analysis of Word Embeddings for Capturing Word SimilaritiesCode0
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking EmphasisCode1
Graph-Embedding Empowered Entity RetrievalCode1
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases—0
Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words—0
The Paradigm Discovery ProblemCode0
Visual Question Answering with Prior Class Semantics—0
Improving Aspect-Level Sentiment Analysis with Aspect Extraction—0
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias MitigationCode1
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?—0
MultiSeg: Parallel Data and Subword Information for Learning Bilingual Embeddings in Low Resource ScenariosCode0
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