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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 3376–3400 of 4002 papers

TitleStatusHype
Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm—0
The Language of Place: Semantic Value from Geospatial Context—0
Robust Training under Linguistic AdversityCode0
Sentiment Analysis of Citations Using Word2vecCode0
Automatic Argumentative-Zoning Using Word2vecCode0
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?—0
Dynamic Bernoulli Embeddings for Language EvolutionCode0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
Story Cloze Ending Selection Baselines and Data Examination—0
What can you do with a rock? Affordance extraction via word embeddings—0
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram FeaturesCode0
Sound-Word2Vec: Learning Word Representations Grounded in Sounds—0
Orthogonalized ALS: A Theoretically Principled Tensor Decomposition Algorithm for Practical Use—0
A Comparative Study of Word Embeddings for Reading Comprehension—0
Dynamic Word Embeddings for Evolving Semantic DiscoveryCode0
Dynamic Word EmbeddingsCode0
Use Generalized Representations, But Do Not Forget Surface Features—0
LTSG: Latent Topical Skip-Gram for Mutually Learning Topic Model and Vector Representations—0
Reproducing and learning new algebraic operations on word embeddings using genetic programmingCode0
Vector Embedding of Wikipedia Concepts and EntitiesCode0
UsingWord Embedding for Cross-Language Plagiarism Detection—0
How to evaluate word embeddings? On importance of data efficiency and simple supervised tasksCode0
Integrating Reviews into Personalized Ranking for Cold Start Recommendation—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—0
Real Multi-Sense or Pseudo Multi-Sense: An Approach to Improve Word Representation—0
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