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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 1351–1360 of 4002 papers

TitleStatusHype
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources—0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Enhancing Scalability of Metric Differential Privacy via Secret Dataset Partitioning and Benders Decomposition—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Enhancing the Inside-Outside Recursive Neural Network Reranker for Dependency Parsing—0
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization—0
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings—0
Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations—0
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling—0
Detecting Semantically Equivalent Questions in Online User Forums—0
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