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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

Showing 14511475 of 4002 papers

TitleStatusHype
Event Ordering with a Generalized Model for Sieve Prediction Ranking0
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch0
Event Role Extraction using Domain-Relevant Word Representations0
Event Role Labelling using a Neural Network Model (\'Etiquetage en r\^oles \'ev\'enementiels fond\'e sur l'utilisation d'un mod\`ele neuronal) [in French]0
Clickbait detection using word embeddings0
Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation0
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus0
Exact gradient updates in time independent of output size for the spherical loss family0
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework0
“Are you calling for the vaporizer you ordered?” Combining Search and Prediction to Identify Orders in Contact Centers0
Example-based Acquisition of Fine-grained Collocation Resources0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity0
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces0
Expanding the Text Classification Toolbox with Cross-Lingual Embeddings0
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity0
Experimental Evaluation of Deep Learning models for Marathi Text Classification0
Experiments on a Guarani Corpus of News and Social Media0
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks0
Explainability of Text Processing and Retrieval Methods: A Critical Survey0
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks0
Detecting Figurative Word Occurrences Using Recurrent Neural Networks0
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis0
Explaining the Trump Gap in Social Distancing Using COVID Discourse0
Explaining Word Embeddings via Disentangled Representation0
Detecting Fake News with Capsule Neural Networks0
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