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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 2226–2250 of 4002 papers

TitleStatusHype
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word VectorsCode0
On the Effect of Low-Frequency Terms on Neural-IR ModelsCode0
Enabling Open-World Specification Mining via Unsupervised Learning—0
Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word VectorsCode0
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding—0
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data—0
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings—0
Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach—0
Understanding the Stability of Medical Concept Embeddings—0
Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings—0
Zero-Shot Cross-Lingual Opinion Target Extraction—0
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings—0
Analytical Methods for Interpretable Ultradense Word EmbeddingsCode0
MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations PredictionCode0
Contextual Aware Joint Probability Model Towards Question Answering System—0
Query Expansion for Cross-Language Question Re-Ranking—0
Text2Node: a Cross-Domain System for Mapping Arbitrary Phrases to a Taxonomy—0
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes—0
Detecting Cybersecurity Events from Noisy Short Text—0
Better Word Embeddings by Disentangling Contextual n-Gram InformationCode0
Mixing syntagmatic and paradigmatic information for concept detection—0
Characterizing the impact of geometric properties of word embeddings on task performanceCode0
Word Similarity Datasets for Thai: Construction and EvaluationCode0
Evaluation of Greek Word Embeddings—0
Text-based depression detection on sparse dataCode0
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