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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2001–2025 of 4002 papers

Title	Date	Tasks	Status	Hype
Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990	Jul 21, 2019	Word Embeddings	CodeCode Available	0
Exploring sentence informativeness	Jul 19, 2019	InformativenessSentence	—Unverified	0
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models	Jul 18, 2019	DecoderMachine Translation	—Unverified	0
Analysis of Word Embeddings Using Fuzzy Clustering	Jul 17, 2019	ClusteringSemantic Similarity	—Unverified	0
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe	Jul 17, 2019	DisentanglementVisual Grounding	—Unverified	0
Multimodal deep networks for text and image-based document classification	Jul 15, 2019	ClassificationDocument Classification	CodeCode Available	0
The Dynamic Embedded Topic Model	Jul 12, 2019	Articlesmodel	CodeCode Available	0
Topic Modeling in Embedding Spaces	Jul 8, 2019	Topic ModelsVariational Inference	CodeCode Available	1
An Intrinsic Nearest Neighbor Analysis of Neural Machine Translation Architectures	Jul 8, 2019	Machine TranslationTranslation	—Unverified	0
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings	Jul 5, 2019	named-entity-recognitionNamed Entity Recognition	CodeCode Available	0
Morphological Word Embeddings	Jul 4, 2019	Word Embeddings	—Unverified	0
Word Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora	Jul 3, 2019	Word Embeddings	CodeCode Available	1
Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings	Jul 3, 2019	ClusteringWord Embeddings	CodeCode Available	0
Neural Semantic Parsing with Anonymization for Command Understanding in General-Purpose Service Robots	Jul 2, 2019	Semantic ParsingWord Embeddings	CodeCode Available	0
Assessing Wordnets with WordNet Embeddings	Jul 1, 2019	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
Visualising WordNet Embeddings: some preliminary results	Jul 1, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Fitting Semantic Relations to Word Embeddings	Jul 1, 2019	Word Embeddings	—Unverified	0
Synthetic, yet natural: Properties of WordNet random walk corpora and the impact of rare words on embedding performance	Jul 1, 2019	Word EmbeddingsWord Similarity	—Unverified	0
Apprentissage de plongements lexicaux par une approche r\'eseaux complexes (Complex networks based word embeddings)	Jul 1, 2019	Word Embeddings	—Unverified	0
Apprentissage de plongements de mots dynamiques avec r\'egularisation de la d\'erive (Learning dynamic word embeddings with drift regularisation)	Jul 1, 2019	ArticlesWord Embeddings	—Unverified	0
Hybridation d'un agent conversationnel avec des plongements lexicaux pour la formation au diagnostic m\'edical (Hybridization of a conversational agent with word embeddings for medical diagnostic training)	Jul 1, 2019	DiagnosticWord Embeddings	—Unverified	0
Exploring Numeracy in Word Embeddings	Jul 1, 2019	Word Embeddings	—Unverified	0
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition	Jul 1, 2019	SentenceSentence Embeddings	CodeCode Available	0
Towards Incremental Learning of Word Embeddings Using Context Informativeness	Jul 1, 2019	Incremental LearningInformativeness	CodeCode Available	0
Better OOV Translation with Bilingual Terminology Mining	Jul 1, 2019	Machine TranslationNMT	—Unverified	0

Show:10 25 50

← PrevPage 81 of 161Next →

No leaderboard results yet.