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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 2201–2250 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
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings—0
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data—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
Better Word Embeddings by Disentangling Contextual n-Gram InformationCode0
Detecting Cybersecurity Events from Noisy Short Text—0
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes—0
Mixing syntagmatic and paradigmatic information for concept detection—0
Characterizing the impact of geometric properties of word embeddings on task performanceCode0
Text-based depression detection on sparse dataCode0
Word Similarity Datasets for Thai: Construction and EvaluationCode0
Evaluation of Greek Word Embeddings—0
Multi-Label Image Recognition with Graph Convolutional NetworksCode0
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples—0
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis—0
Alternative Weighting Schemes for ELMo EmbeddingsCode0
Effective Context and Fragment Feature Usage for Named Entity Recognition—0
Gender Bias in Contextualized Word EmbeddingsCode1
Text Classification Components for Detecting Descriptions and Names of CAD models—0
Generative Adversarial Networks for text using word2vec intermediariesCode0
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited DataCode1
Density Matching for Bilingual Word EmbeddingCode0
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence LabelingCode0
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems—0
Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF—0
Probing Biomedical Embeddings from Language ModelsCode0
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word EmbeddingsCode0
Identification, Interpretability, and Bayesian Word EmbeddingsCode0
Attentive Mimicking: Better Word Embeddings by Attending to Informative ContextsCode0
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation—0
Unsupervised Abbreviation Disambiguation Contextual disambiguation using word embeddings—0
Multimodal Machine Translation with Embedding PredictionCode0
SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings EvaluationCode0
Integrating Semantic Knowledge to Tackle Zero-shot Text ClassificationCode0
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition—0
Learning semantic sentence representations from visually grounded language without lexical knowledgeCode0
Deep Learning and Word Embeddings for Tweet Classification for Crisis Response—0
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