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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 3031–3040 of 4002 papers

TitleStatusHype
NLP and Education: using semantic similarity to evaluate filled gaps in a large-scale Cloze test in the classroom—0
NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets—0
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings—0
Noisy Parallel Corpus Filtering through Projected Word Embeddings—0
Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input—0
Non-Complementarity of Information in Word-Embedding and Brain Representations in Distinguishing between Concrete and Abstract Words—0
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces—0
Non-Linearity in Mapping Based Cross-Lingual Word Embeddings—0
Non-Linear Relational Information Probing in Word Embeddings—0
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