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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 2121–2130 of 4002 papers

TitleStatusHype
English-Malay Cross-Lingual Embedding Alignment using Bilingual Lexicon Augmentation—0
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network—0
English Resource Semantics—0
English WordNet Random Walk Pseudo-Corpora—0
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
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