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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 1851–1875 of 4002 papers

TitleStatusHype
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model PerformanceCode0
Transformers without Tears: Improving the Normalization of Self-AttentionCode0
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy LabelsCode0
Mapping Supervised Bilingual Word Embeddings from English to low-resource languagesCode0
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
Aligning Multilingual Word Embeddings for Cross-Modal Retrieval TaskCode0
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish—0
Correlations between Word Vector SetsCode0
On Dimensional Linguistic Properties of the Word Embedding SpaceCode0
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text DatasetsCode0
DialectGram: Detecting Dialectal Variation at Multiple Geographic ResolutionsCode0
Complex networks based word embeddings—0
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Specializing Word Embeddings (for Parsing) by Information BottleneckCode0
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word RepresentationsCode0
Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts—0
Additional Shared Decoder on Siamese Multi-view Encoders for Learning Acoustic Word Embeddings—0
Language-Agnostic Visual-Semantic EmbeddingsCode0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models—0
Learning Category Correlations for Multi-label Image Recognition with Graph Networks—0
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages—0
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution—0
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