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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 3351–3400 of 4002 papers

TitleStatusHype
Efficient, Compositional, Order-sensitive n-gram EmbeddingsCode0
Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language—0
Real-Time Keyword Extraction from Conversations—0
Attention Modeling for Targeted Sentiment—0
Measuring Topic Coherence through Optimal Word Buckets—0
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit—0
Learning User Embeddings from Emails—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Nonsymbolic Text Representation—0
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis—0
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations—0
Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases—0
Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets—0
PP Attachment: Where do We Stand?—0
Learning to Negate Adjectives with Bilinear Models—0
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation—0
How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Multilingual Training of Crosslingual Word Embeddings—0
Grouping business news stories based on salience of named entities—0
Delexicalized Word Embeddings for Cross-lingual Dependency Parsing—0
Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings—0
Lexical Simplification with Neural Ranking—0
Analyzing Semantic Change in Japanese Loanwords—0
Inducing Embeddings for Rare and Unseen Words by Leveraging Lexical Resources—0
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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