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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 761–770 of 4002 papers

TitleStatusHype
Co-learning of Word Representations and Morpheme Representations—0
A semi-supervised model for Persian rumor verification based on content information—0
A Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media—0
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings—0
Combining BERT with Static Word Embeddings for Categorizing Social Media—0
Combining Character and Word Embeddings for the Detection of Offensive Language in Arabic—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
A House United: Bridging the Script and Lexical Barrier between Hindi and Urdu—0
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