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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 276–300 of 4002 papers

TitleStatusHype
Argument from Old Man’s View: Assessing Social Bias in Argumentation—0
An analysis of the user occupational class through Twitter content—0
An Analysis of Hierarchical Text Classification Using Word Embeddings—0
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition—0
Adversarial Contrastive Estimation—0
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks—0
A Comparative Study of Word Embeddings for Reading Comprehension—0
A Review of Cross-Domain Text-to-SQL Models—0
An Analysis of Deep Contextual Word Embeddings and Neural Architectures for Toponym Mention Detection in Scientific Publications—0
Analyzing Word Embedding Through Structural Equation Modeling—0
Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture—0
A comparative study of word embeddings and other features for lexical complexity detection in French—0
Advancing Fake News Detection: Hybrid DeepLearning with FastText and Explainable AI—0
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data—0
A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings—0
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content—0
Analyzing the Representational Geometry of Acoustic Word Embeddings—0
Analyzing the Limitations of Cross-lingual Word Embedding Mappings—0
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention—0
Analyzing the Framing of 2020 Presidential Candidates in the News—0
A Dual Embedding Space Model for Document Ranking—0
A Comparative Study of Transformers on Word Sense Disambiguation—0
Disentangling Latent Emotions of Word Embeddings on Complex Emotional Narratives—0
Analyzing Semantic Change in Japanese Loanwords—0
Analyzing Correlations Between Intrinsic and Extrinsic Bias Metrics of Static Word Embeddings With Their Measuring Biases Aligned—0
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