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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 1901–1910 of 4002 papers

TitleStatusHype
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Intrinsic Bias Metrics Do Not Correlate with Application Bias—0
Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings—0
Derivational Morphological Relations in Word Embeddings—0
Derivational Morphological Relations in Word Embeddings—0
Cross-Lingual Word Embeddings and the Structure of the Human Bilingual Lexicon—0
BERT-based Ranking for Biomedical Entity Normalization—0
Cross-lingual Word Embeddings beyond Zero-shot Machine Translation—0
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media—0
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