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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 3781–3790 of 4002 papers

TitleStatusHype
Enhanced Word Representations for Bridging Anaphora Resolution—0
Enhancing Automatic Wordnet Construction Using Word Embeddings—0
Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques—0
Enhancing Clinical Concept Extraction with Contextual Embeddings—0
Enhancing General Sentiment Lexicons for Domain-Specific Use—0
Enhancing Interpretability using Human Similarity Judgements to Prune Word Embeddings—0
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources—0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
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