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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 2451–2460 of 4002 papers

TitleStatusHype
Predicting News Headline Popularity with Syntactic and Semantic Knowledge Using Multi-Task Learning—0
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation—0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations—0
Modeling Input Uncertainty in Neural Network Dependency ParsingCode0
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings—0
InferLite: Simple Universal Sentence Representations from Natural Language Inference Data—0
Quantifying Context Overlap for Training Word Embeddings—0
Unsupervised Bilingual Lexicon Induction via Latent Variable Models—0
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
Synthetic Data Made to Order: The Case of ParsingCode0
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