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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 3726–3750 of 4002 papers

TitleStatusHype
Local Word Vectors Guiding Keyphrase ExtractionCode0
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference Resolution.Code0
Fast and Robust Comparison of Probability Measures in Heterogeneous SpacesCode0
The Lifted Matrix-Space Model for Semantic CompositionCode0
Churn Intent Detection in Multilingual Chatbot Conversations and Social MediaCode0
The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical NotesCode0
The Limitations of Cross-language Word Embeddings EvaluationCode0
ChatGPT-guided Semantics for Zero-shot LearningCode0
A Causal Inference Method for Reducing Gender Bias in Word Embedding RelationsCode0
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary InductionCode0
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy LabelsCode0
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain ResponsesCode0
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling MechanismsCode0
A Hierarchically-Labeled Portuguese Hate Speech DatasetCode0
Pretrained Transformers for Simple Question Answering over Knowledge GraphsCode0
Simple and Effective Dimensionality Reduction for Word EmbeddingsCode0
An embedded segmental K-means model for unsupervised segmentation and clustering of speechCode0
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word EmbeddingsCode0
Few-shot Learning for Named Entity Recognition in Medical TextCode0
Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated SolutionCode0
Few-Shot Representation Learning for Out-Of-Vocabulary WordsCode0
Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the LoopCode0
A Semi-supervised Framework for Image CaptioningCode0
Ultradense Word Embeddings by Orthogonal TransformationCode0
Analyzing the Surprising Variability in Word Embedding Stability Across LanguagesCode0
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