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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 3681–3690 of 4002 papers

TitleStatusHype
Distribution is not enough: going Firther—0
Disunited Nations? A Multiplex Network Approach to Detecting Preference Affinity Blocs using Texts and Votes—0
Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects?—0
DLRG@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil using Multilingual Transformer Models—0
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings—0
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
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
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