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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 1561–1570 of 4002 papers

TitleStatusHype
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages—0
Fermi at SemEval-2017 Task 7: Detection and Interpretation of Homographic puns in English Language—0
Addressing Low-Resource Scenarios with Character-aware Embeddings—0
A Chinese Writing Correction System for Learning Chinese as a Foreign Language—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Field Embedding: A Unified Grain-Based Framework for Word Representation—0
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of Words—0
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference—0
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