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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 3021–3030 of 4002 papers

TitleStatusHype
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model—0
NEUROSENT-PDI at SemEval-2018 Task 7: Discovering Textual Relations With a Neural Network Model—0
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation—0
Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation—0
New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations—0
New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings—0
Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics—0
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis—0
NLP Analytics in Finance with DoRe: A French 250M Tokens Corpus of Corporate Annual Reports—0
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