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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 2326–2350 of 4002 papers

TitleStatusHype
From communities to interpretable network and word embedding: an unified approach—0
From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings—0
From Language to Language-ish: How Brain-Like is an LSTM's Representation of Nonsensical Language Stimuli?—0
From meaning to perception -- exploring the space between word and odor perception embeddings—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
From Raw Text to Universal Dependencies - Look, No Tags!—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers—0
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers—0
Fully Delexicalized Contexts for Syntax-Based Word Embeddings—0
funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts—0
funSentiment at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs Using Word Vectors Built from StockTwits and Twitter—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion approaches for emotion recognition from speech using acoustic and text-based features—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings—0
Gender bias Evaluation in Luganda-English Machine Translation—0
Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives—0
Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics—0
Gender Prediction for Chinese Social Media Data—0
Gender Roles from Word Embeddings in a Century of Children’s Books—0
Generating Adequate Distractors for Multiple-Choice Questions—0
Generating Varied Training Corpora in Runyankore Using a Combined Semantic and Syntactic, Pattern-Grammar-based Approach—0
Generic and Specialized Word Embeddings for Multi-Domain Machine Translation—0
Generic Embedding-Based Lexicons for Transparent and Reproducible Text Scoring—0
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