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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 751–775 of 4002 papers

TitleStatusHype
discopy: A Neural System for Shallow Discourse ParsingCode1
Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces—0
Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions—0
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index—0
The Golden Rule as a Heuristic to Measure the Fairness of Texts Using Machine Learning—0
Learning Personal Food Preferences via Food Logs EmbeddingCode0
Measuring a Texts Fairness Dimensions Using Machine Learning Based on Social Psychological Factors—0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Hate and Offensive Speech Detection in Hindi and Marathi—0
Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language DataCode0
Inter-Sense: An Investigation of Sensory Blending in Fiction—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Subword-based Cross-lingual Transfer of Embeddings from Hindi to Marathi—0
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models—0
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Large Scale Substitution-based Word Sense Induction—0
BI-RADS BERT & Using Section Segmentation to Understand Radiology ReportsCode0
Evaluating Off-the-Shelf Machine Listening and Natural Language Models for Automated Audio Captioning—0
Regionalized models for Spanish language variations based on Twitter—0
Offensive Language Detection with BERT-based models, By Customizing Attention Probabilities—0
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference ResolutionCode0
Using Word Embeddings for Italian Crime News Categorization—0
Human-in-the-Loop Refinement of Word Embeddings—0
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy—0
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