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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 2421–2430 of 4002 papers

TitleStatusHype
The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations—0
The Sentimental Value of Chinese Sub-Character Components—0
The strange geometry of skip-gram with negative sampling—0
The TALP--UPC Spanish--English WMT Biomedical Task: Bilingual Embeddings and Char-based Neural Language Model Rescoring in a Phrase-based System—0
The (too Many) Problems of Analogical Reasoning with Word Vectors—0
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings—0
The Visualization of Change in Word Meaning over Time using Temporal Word Embeddings—0
The Word Analogy Testing Caveat—0
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples—0
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions—0
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