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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 1361–1370 of 4002 papers

TitleStatusHype
Enriching Complex Networks with Word Embeddings for Detecting Mild Cognitive Impairment from Speech Transcripts—0
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings—0
Enriching Word Embeddings with Domain Knowledge for Readability Assessment—0
Censorship of Online Encyclopedias: Implications for NLP Models—0
A Progressive Learning Approach to Chinese SRL Using Heterogeneous Data—0
Ensemble Methods to Distinguish Mainland and Taiwan Chinese—0
Ensemble Romanian Dependency Parsing with Neural Networks—0
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification—0
Entity-Centric Contextual Affective Analysis—0
Detecting Semantically Equivalent Questions in Online User Forums—0
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