SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3601–3650 of 4002 papers

TitleStatusHype
An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network—0
Neural Networks For Negation Scope Detection—0
Word Embedding Calculus in Meaningful Ultradense Subspaces—0
How to Train good Word Embeddings for Biomedical NLPCode0
Word embeddings and discourse information for Quality Estimation—0
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
Word Embeddings with Limited Memory—0
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network—0
Semantics-Driven Recognition of Collocations Using Word Embeddings—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Improved Semantic Representation for Domain-Specific Entities—0
Intrinsic Evaluations of Word Embeddings: What Can We Do Better?—0
Investigating Language Universal and Specific Properties in Word Embeddings—0
Is ``Universal Syntax'' Universally Useful for Learning Distributed Word Representations?—0
Jointly Learning to Embed and Predict with Multiple Languages—0
Sentence Embedding Evaluation Using Pyramid Annotation—0
Visual Relationship Detection with Language Priors—0
Cseq2seq: Cyclic Sequence-to-Sequence Learning—0
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word EmbeddingsCode0
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding GenerationCode0
Language classification from bilingual word embedding graphs—0
Enriching Word Vectors with Subword InformationCode0
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction—0
Mapping distributional to model-theoretic semantic spaces: a baselineCode0
Actionable and Political Text Classification using Word Embeddings and LSTM—0
Representation learning for very short texts using weighted word embedding aggregationCode0
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic ResourceCode0
Learning Crosslingual Word Embeddings without Bilingual CorporaCode0
Exact gradient updates in time independent of output size for the spherical loss family—0
Using Word Embeddings for Automatic Query Expansion—0
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation—0
Toward Word Embedding for Personalized Information Retrieval—0
Using Word Embeddings in Twitter Election Classification—0
An empirical study on large scale text classification with skip-gram embeddings—0
Neural Morphological Tagging from Characters for Morphologically Rich Languages—0
Quantifying and Reducing Stereotypes in Word Embeddings—0
Siamese CBOW: Optimizing Word Embeddings for Sentence RepresentationsCode0
Learning Word Sense Embeddings from Word Sense Definitions—0
Active Discriminative Text Representation Learning—0
PSDVec: a Toolbox for Incremental and Scalable Word Embedding—0
Inducing Domain-Specific Sentiment Lexicons from Unlabeled CorporaCode0
Sentence Similarity Measures for Fine-Grained Estimation of Topical Relevance in Learner Essays—0
A Joint Model for Word Embedding and Word Morphology—0
Multilingual Visual Sentiment Concept Matching—0
Automated Image Captioning for Rapid Prototyping and Resource Constrained Environments—0
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction—0
LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies—0
Learning a POS tagger for AAVE-like language—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Show:102550
← PrevPage 73 of 81Next →

No leaderboard results yet.