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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 1801–1825 of 4002 papers

TitleStatusHype
Textual Data for Time Series Forecasting—0
Wasserstein distances for evaluating cross-lingual embeddings—0
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings—0
Lucene for Approximate Nearest-Neighbors Search on Arbitrary Dense Vectors—0
Towards Automated Website Classification by Deep Learning—0
Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity—0
An Improved Historical Embedding without Alignment—0
Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings—0
Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates—0
Keyphrase Extraction from Disaster-related Tweets—0
Meemi: A Simple Method for Post-processing and Integrating Cross-lingual Word Embeddings—0
A Probabilistic Framework for Learning Domain Specific Hierarchical Word Embeddings—0
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model PerformanceCode0
Joint Learning of Word and Label Embeddings for Sequence Labelling in Spoken Language Understanding—0
Mapping Supervised Bilingual Word Embeddings from English to low-resource languagesCode0
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy LabelsCode0
Transformers without Tears: Improving the Normalization of Self-AttentionCode0
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-CorrectionCode0
IdBench: Evaluating Semantic Representations of Identifier Names in Source CodeCode0
Multi-label Categorization of Accounts of Sexism using a Neural FrameworkCode0
Structured Pruning of Large Language ModelsCode1
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish—0
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval TaskCode0
Correlations between Word Vector SetsCode0
On Dimensional Linguistic Properties of the Word Embedding SpaceCode0
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