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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 2526–2550 of 4002 papers

TitleStatusHype
Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings—0
Two Stages Approach for Tweet Engagement Prediction—0
UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment—0
UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task—0
UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA—0
UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?—0
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement—0
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings—0
UMDuluth-CS8761 at SemEval-2018 Task9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings—0
UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones—0
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation—0
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes—0
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings—0
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages—0
Understanding and Improving Multi-Sense Word Embeddings via Extended Robust Principal Component Analysis—0
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models—0
Understanding the Stability of Medical Concept Embeddings—0
Understanding the Source of Semantic Regularities in Word Embeddings—0
Understanding Undesirable Word Embedding Associations—0
UnibucKernel: A kernel-based learning method for complex word identification—0
Uniform Discretized Integrated Gradients: An effective attribution based method for explaining large language models—0
Unifying Bayesian Inference and Vector Space Models for Improved Decipherment—0
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures—0
UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing—0
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification—0
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