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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 1961–1970 of 4002 papers

TitleStatusHype
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Investigating the Stability of Concrete Nouns in Word Embeddings—0
Dependency Parsing for Urdu: Resources, Conversions and Learning—0
IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
Dependency-Based Word Embeddings—0
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