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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 2031–2040 of 4002 papers

TitleStatusHype
DNN-Based Semantic Model for Rescoring N-best Speech Recognition List—0
Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF—0
Document-Level Machine Translation with Word Vector Models—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations—0
Do gender neutral affixes naturally reduce gender bias in static word embeddings?—0
Domain adaptation challenges of BERT in tokenization and sub-word representations of Out-of-Vocabulary words—0
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