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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 3726–3750 of 4002 papers

TitleStatusHype
Extracting Temporal and Causal Relations between Events—0
Syntactic and semantic classification of verb arguments using dependency-based and rich semantic features—0
Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints—0
From Incremental Meaning to Semantic Unit (phrase by phrase)Code0
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation—0
An Ensemble Method to Produce High-Quality Word Embeddings (2016)Code2
Nonparametric Spherical Topic Modeling with Word EmbeddingsCode0
Cross-lingual Models of Word Embeddings: An Empirical ComparisonCode0
Bilingual Learning of Multi-sense Embeddings with Discrete AutoencodersCode0
Part-of-Speech Relevance Weights for Learning Word Embeddings—0
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings—0
Topic Modeling Using Distributed Word Embeddings—0
Multichannel Variable-Size Convolution for Sentence Classification—0
Neural Discourse Relation Recognition with Semantic Memory—0
Part-of-Speech Tagging for Historical English—0
Unsupervised word segmentation and lexicon discovery using acoustic word embeddings—0
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification—0
Character-based Neural Machine Translation—0
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs—0
Easy-First Dependency Parsing with Hierarchical Tree LSTMs—0
Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec ApproachCode0
Representation of linguistic form and function in recurrent neural networksCode0
Toward Mention Detection Robustness with Recurrent Neural Networks—0
Ultradense Word Embeddings by Orthogonal TransformationCode0
Recovering Structured Probability Matrices—0
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