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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 801–810 of 4002 papers

TitleStatusHype
AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine—0
Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Comparison between Voting Classifier and Deep Learning methods for Arabic Dialect Identification—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Comparison of Representations of Named Entities for Document Classification—0
Comparison of Short-Text Sentiment Analysis Methods for Croatian—0
A Hmong Corpus with Elaborate Expression Annotations—0
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