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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 2051–2100 of 4002 papers

TitleStatusHype
DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection—0
Do We Need Neural Models to Explain Human Judgments of Acceptability?—0
The Influence of Down-Sampling Strategies on SVD Word Embedding Stability—0
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—0
Drop-out Conditional Random Fields for Twitter with Huge Mined Gazetteer—0
DS at SemEval-2019 Task 9: From Suggestion Mining with neural networks to adversarial cross-domain classification—0
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations—0
DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator—0
Dual Embeddings and Metrics for Relational Similarity—0
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations—0
Du bon usage d'ingr\'edients linguistiques sp\'eciaux pour classer des recettes exceptionnelles (Using Special Linguistic Ingredients to Classify Exceptional Recipes )—0
Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings—0
Early Discovery of Disappearing Entities in Microblogs—0
Earth Mover's Distance Minimization for Unsupervised Bilingual Lexicon Induction—0
Easy-First Dependency Parsing with Hierarchical Tree LSTMs—0
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter—0
ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation—0
EDGAR-CORPUS: Billions of Tokens Make The World Go Round—0
EduBERT: Pretrained Deep Language Models for Learning Analytics—0
Effective Context and Fragment Feature Usage for Named Entity Recognition—0
Effective Greedy Inference for Graph-based Non-Projective Dependency Parsing—0
Effectiveness of Deep Networks in NLP using BiDAF as an example architecture—0
Effect of dimensionality change on the bias of word embeddings—0
Effect of Text Color on Word Embeddings—0
Effect of Text Processing Steps on Twitter Sentiment Classification using Word Embedding—0
Effects of Creativity and Cluster Tightness on Short Text Clustering Performance—0
Effects of Word Embeddings on Neural Network-based Pitch Accent Detection—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora—0
Efficient Extraction of Pseudo-Parallel Sentences from Raw Monolingual Data Using Word Embeddings—0
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space—0
EICA Team at SemEval-2018 Task 2: Semantic and Metadata-based Features for Multilingual Emoji Prediction—0
ELiRF-UPV at SemEval-2017 Task 4: Sentiment Analysis using Deep Learning—0
ELiRF-UPV at SemEval-2017 Task 7: Pun Detection and Interpretation—0
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge—0
Elucidating Conceptual Properties from Word Embeddings—0
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic—0
Embedding Grammars—0
Embeddings as representation for symbolic music—0
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature—0
Embeddings in Natural Language Processing—0
Embedding Space Correlation as a Measure of Domain Similarity—0
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
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