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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 3951–3975 of 4002 papers

TitleStatusHype
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations PredictionCode0
Spoken Word2Vec: Learning Skipgram Embeddings from SpeechCode0
Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information PreservingCode0
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Identification of Adjective-Noun Neologisms using Pretrained Language ModelsCode0
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword RepresentationsCode0
Analysis of Railway Accidents' Narratives Using Deep LearningCode0
A Review of Different Word Embeddings for Sentiment Classification using Deep LearningCode0
Topic Modeling on User Stories using Word Mover's DistanceCode0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
Topic Modeling over Short Texts by Incorporating Word EmbeddingsCode0
Acquiring Common Sense Spatial Knowledge through Implicit Spatial TemplatesCode0
[RE] Double-Hard Debias: Tailoring Word Embeddings for Gender Bias MitigationCode0
Morphology-Aware Meta-Embeddings for TamilCode0
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token EncodingsCode0
IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual TextsCode0
Automatic Extraction of Nested Entities in Clinical Referrals in SpanishCode0
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous OutputsCode0
MoRTy: Unsupervised Learning of Task-specialized Word Embeddings by AutoencodingCode0
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular MaximizationCode0
StarSpace: Embed All The Things!Code0
Automatic Detection of Sexist Statements Commonly Used at the WorkplaceCode0
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