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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 3221–3230 of 4002 papers

TitleStatusHype
Bag-of-Words Transfer: Non-Contextual Techniques for Multi-Task Learning—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Balancing the composition of word embeddings across heterogenous data sets—0
Batch IS NOT Heavy: Learning Word Representations From All Samples—0
Bayesian Paragraph Vectors—0
BB\_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs—0
Beats of Bias: Analyzing Lyrics with Topic Modeling and Gender Bias Measurements—0
Bench-Marking And Improving Arabic Automatic Image Captioning Through The Use Of Multi-Task Learning Paradigm—0
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
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