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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 2701–2750 of 4002 papers

TitleStatusHype
Learning Relation Representations from Word Representations—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
Learning Representations for Detecting Abusive Language—0
Learning Representations for Text-level Discourse Parsing—0
Learning Representations of Entities and Relations—0
Learning Semantic Hierarchies via Word Embeddings—0
Learning Semantic Relatedness From Human Feedback Using Metric Learning—0
Learning Semantic Similarity for Very Short Texts—0
Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints—0
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy—0
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy—0
Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources—0
Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification—0
Learning Stock Market Sentiment Lexicon and Sentiment-Oriented Word Vector from StockTwits—0
Learning Structured Semantic Embeddings for Visual Recognition—0
Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network—0
Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network—0
Learning the Dimensionality of Word Embeddings—0
Learning to Compose Spatial Relations with Grounded Neural Language Models—0
Learning to Compute Word Embeddings On the Fly—0
Learning to Generate Word Representations using Subword Information—0
Learning to Lemmatize in the Word Representation Space—0
Learning to Name Classes for Vision and Language Models—0
Learning to Negate Adjectives with Bilinear Models—0
Learning to Rank Broad and Narrow Queries in E-Commerce—0
Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings—0
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks—0
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Learning User Embeddings from Emails—0
Learning word embeddings efficiently with noise-contrastive estimation—0
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets—0
Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Learning Word Embeddings from Intrinsic and Extrinsic Views—0
Learning Word Embeddings from Speech—0
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects—0
Learning Word Embeddings without Context Vectors—0
Learning Word Meta-Embeddings—0
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces—0
Learning Word Representations with Regularization from Prior Knowledge—0
Learning Word Sense Embeddings from Word Sense Definitions—0
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution—0
Learnt Contrastive Concept Embeddings for Sign Recognition—0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service—0
Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing—0
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models—0
Lessons in Reproducibility: Insights from NLP Studies in Materials Science—0
Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet—0
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