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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 3601–3625 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
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