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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 3751–3775 of 4002 papers

TitleStatusHype
Embedding Structured Dictionary Entries—0
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training—0
Embodying Pre-Trained Word Embeddings Through Robot Actions—0
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging—0
Emerging Cross-lingual Structure in Pretrained Language Models—0
EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning—0
EMOMINER at SemEval-2019 Task 3: A Stacked BiLSTM Architecture for Contextual Emotion Detection in Text—0
EmoNLP at IEST 2018: An Ensemble of Deep Learning Models and Gradient Boosting Regression Tree for Implicit Emotion Prediction in Tweets—0
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network—0
Emotion-Cause Pair Extraction in Customer Reviews—0
Emotion Enriched Retrofitted Word Embeddings—0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
Empirical Analysis of Image Caption Generation using Deep Learning—0
Empirical Autopsy of Deep Video Captioning Frameworks—0
Empirical Study of Diachronic Word Embeddings for Scarce Data—0
Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction—0
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts—0
Empty Category Detection using Path Features and Distributed Case Frames—0
Enabling Cognitive Intelligence Queries in Relational Databases using Low-dimensional Word Embeddings—0
Enabling Open-World Specification Mining via Unsupervised Learning—0
En-Ar Bilingual Word Embeddings without Word Alignment: Factors Effects—0
Encoders Help You Disambiguate Word Senses in Neural Machine Translation—0
Encoding Prior Knowledge with Eigenword Embeddings—0
Encoding Sentiment Information into Word Vectors for Sentiment Analysis—0
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings—0
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