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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 501–510 of 4002 papers

TitleStatusHype
Count-Based and Predictive Language Models for Exploring DeReKo—0
Enhancing Deep Learning with Embedded Features for Arabic Named Entity RecognitionCode0
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese—0
XLNET-GRU Sentiment Regression Model for Cryptocurrency News in English and Malay—0
Use Case: Romanian Language Resources in the LOD Paradigm—0
Accurate Dependency Parsing and Tagging of Latin—0
Dialects Identification of Armenian Language—0
Evaluating Monolingual and Crosslingual Embeddings on Datasets of Word Association Norms—0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Cross-lingual Linking of Automatically Constructed Frames and FrameNet—0
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