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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 1731–1740 of 4002 papers

TitleStatusHype
Hybrid Text Feature Modeling for Disease Group Prediction using Unstructured Physician Notes—0
Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction—0
A Causal Inference Method for Reducing Gender Bias in Word Embedding RelationsCode0
Towards robust word embeddings for noisy textsCode0
Causally Denoise Word Embeddings Using Half-Sibling RegressionCode0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Anaphora Resolution in Dialogue Systems for South Asian Languages—0
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context—0
Multilingual Culture-Independent Word Analogy Datasets—0
Empirical Autopsy of Deep Video Captioning Frameworks—0
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