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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 1211–1220 of 4002 papers

TitleStatusHype
An efficient domain-independent approach for supervised keyphrase extraction and ranking—0
Adversarial Training for Unsupervised Bilingual Lexicon Induction—0
A Comparison of Context-sensitive Models for Lexical Substitution—0
A Bayesian approach to uncertainty in word embedding bias estimation—0
DCU: Using Distributional Semantics and Domain Adaptation for the Semantic Textual Similarity SemEval-2015 Task 2—0
DCC-Uchile at SemEval-2020 Task 1: Temporal Referencing Word Embeddings—0
DataStories at SemEval-2017 Task 6: Siamese LSTM with Attention for Humorous Text Comparison—0
Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia—0
An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network—0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
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