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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 811–820 of 4002 papers

TitleStatusHype
Taxonomy Enrichment with Text and Graph Vector Representations—0
Evaluating the timing and magnitude of semantic change in diachronic word embedding models—0
Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency—0
Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability StudyCode0
Evaluating Machine Common Sense via Cloze Testing—0
Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task—0
Tracking Legislators’ Expressed Policy Agendas in Real TimeCode0
Impart Contextualization to Static Word Embeddings through Semantic Relations—0
Topic Modeling with Topological Data Analysis—0
Revisiting Additive Compositionality: AND, OR, and NOT Operations with Word Embeddings—0
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