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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 2511–2520 of 4002 papers

TitleStatusHype
Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function—0
Word Equations: Inherently Interpretable Sparse Word Embeddingsthrough Sparse Coding—0
Initial Experiments in Data-Driven Morphological Analysis for Finnish—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings—0
Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings—0
In Neural Machine Translation, What Does Transfer Learning Transfer?—0
Inspecting the concept knowledge graph encoded by modern language models—0
Instantiation—0
Integrating Dictionary Feature into A Deep Learning Model for Disease Named Entity Recognition—0
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