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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 3131–3140 of 4002 papers

TitleStatusHype
ASR error management for improving spoken language understanding—0
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media—0
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP—0
Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax—0
Associating Neural Word Embeddings With Deep Image Representations Using Fisher Vectors—0
Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics—0
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments—0
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable—0
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