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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 2181–2190 of 4002 papers

TitleStatusHype
Pretrained Transformers for Simple Question Answering—0
Investigating the Stability of Concrete Nouns in Word Embeddings—0
Predicting Word Concreteness and Imagery—0
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs—0
On Learning Word Embeddings From Linguistically Augmented Text Corpora—0
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts—0
A Comparison of Context-sensitive Models for Lexical Substitution—0
Semantic Frame Embeddings for Detecting Relations between Software Requirements—0
Aligning Open IE Relations and KB Relations using a Siamese Network Based on Word Embedding—0
Language-Agnostic Model for Aspect-Based Sentiment Analysis—0
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