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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 1801–1810 of 4002 papers

TitleStatusHype
Implicit Phenomena in Short-answer Scoring Data—0
Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings—0
Improving Zero Shot Learning Baselines with Commonsense Knowledge—0
Importance of Self-Attention for Sentiment Analysis—0
Indigenous Language Revitalization and the Dilemma of Gender Bias—0
Improved and Robust Controversy Detection in General Web Pages Using Semantic Approaches under Large Scale Conditions—0
Des pseudo-sens pour am\'eliorer l'extraction de synonymes \`a partir de plongements lexicaux (Pseudo-senses for improving the extraction of synonyms from word embeddings)—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Improved CCG Parsing with Semi-supervised Supertagging—0
Designing a Russian Idiom-Annotated Corpus—0
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