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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 1491–1500 of 4002 papers

TitleStatusHype
Comparing in context: Improving cosine similarity measures with a metric tensor—0
A multilabel approach to morphosyntactic probing—0
A Deeper Look into Dependency-Based Word Embeddings—0
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping—0
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Comparing Contextual and Static Word Embeddings with Small Data—0
Aspect-Based Sentiment Analysis Using Bitmask Bidirectional Long Short Term Memory Networks—0
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition—0
Comparing Approaches for Automatic Question Identification—0
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