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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 1081–1090 of 4002 papers

TitleStatusHype
Evaluating Contextualized Representations of (Spanish) Ambiguous Words: A New Lexical Resource and Empirical Analysis—0
An Improved Neural Baseline for Temporal Relation Extraction—0
Bidirectional Retrieval Made Simple—0
Bidirectional Recurrent Convolutional Neural Network for Relation Classification—0
An Improved Historical Embedding without Alignment—0
Aggregating Continuous Word Embeddings for Information Retrieval—0
A Computational Approach to Measuring the Semantic Divergence of Cognates—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Different Contexts Lead to Different Word Embeddings—0
Bidirectional Long Short-Term Memory Networks for Relation Classification—0
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