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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 1071–1080 of 4002 papers

TitleStatusHype
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings—0
DICoE@FinSim-3: Financial Hypernym Detection using Augmented Terms and Distance-based Features—0
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder—0
Disambiguated skip-gram model—0
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition—0
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representations on Sequence Labelling Tasks—0
Evaluating Contextualized Representations of (Spanish) Ambiguous Words: A New Lexical Resource and Empirical Analysis—0
An Improved Neural Baseline for Temporal Relation Extraction—0
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