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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 1471–1480 of 4002 papers

TitleStatusHype
Complementary Strategies for Low Resourced Morphological Modeling—0
Compiling a Highly Accurate Bilingual Lexicon by Combining Different Approaches—0
Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets—0
A Multimodal Approach towards Emotion Recognition of Music using Audio and Lyrical Content—0
Comparison of Short-Text Sentiment Analysis Methods for Croatian—0
Comparison of Representations of Named Entities for Document Classification—0
Comparison of Paragram and GloVe Results for Similarity Benchmarks—0
Assessing Polyseme Sense Similarity through Co-predication Acceptability and Contextualised Embedding Distance—0
A multi-level approach for hierarchical Ticket Classification—0
A Deep Fusion Model for Domain Adaptation in Phrase-based MT—0
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