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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 791–800 of 4002 papers

TitleStatusHype
Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence—0
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity—0
Blind signal decomposition of various word embeddings based on join and individual variance explained—0
BLCU\_NLP at SemEval-2018 Task 12: An Ensemble Model for Argument Reasoning Based on Hierarchical Attention—0
Comparing Approaches for Automatic Question Identification—0
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition—0
Comparing Contextual and Static Word Embeddings with Small Data—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Comparing in context: Improving cosine similarity measures with a metric tensor—0
Antonymy-Synonymy Discrimination through the Repelling Parasiamese Neural Network—0
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