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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 401–450 of 4002 papers

TitleStatusHype
Debiasing Sentence Embedders through Contrastive Word PairsCode0
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level AttacksCode0
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili—0
Advancing Fake News Detection: Hybrid DeepLearning with FastText and Explainable AI—0
A comparative analysis of embedding models for patent similarity—0
An efficient domain-independent approach for supervised keyphrase extraction and ranking—0
Empowering Segmentation Ability to Multi-modal Large Language ModelsCode0
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction—0
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech RepresentationsCode0
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis—0
The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical NotesCode0
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources—0
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic ChangeCode0
Ontology Enhanced Claim Detection—0
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings—0
Word Embeddings Revisited: Do LLMs Offer Something New?—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
Semi-Supervised Learning for Bilingual Lexicon InductionCode0
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts—0
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random FeaturesCode0
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition—0
Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing—0
Graph-based Clustering for Detecting Semantic Change Across Time and LanguagesCode0
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding AlteringCode0
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMsCode0
Multi-class Regret Detection in Hindi Devanagari Script—0
Semantic Properties of cosine based bias scores for word embeddingsCode0
CERM: Context-aware Literature-based Discovery via Sentiment Analysis—0
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings—0
Contrastive Learning in Distilled ModelsCode0
Multilingual acoustic word embeddings for zero-resource languages—0
GWPT: A Green Word-Embedding-based POS Tagger—0
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed EmbeddingsCode0
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research—0
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer—0
Estimating Text Similarity based on Semantic Concept Embeddings—0
Building Vision-Language Models on Solid Foundations with Masked Distillation—0
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks—0
Effect of dimensionality change on the bias of word embeddings—0
Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch—0
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations—0
Disentangling continuous and discrete linguistic signals in transformer-based sentence embeddings—0
Def2Vec: Extensible Word Embeddings from Dictionary DefinitionsCode0
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
Natural Language Processing for Diagnosis and Risk Assessment of Cardiovascular Disease—0
Where exactly does contextualization in a PLM happen?—0
Constructing Vec-tionaries to Extract Message Features from Texts: A Case Study of Moral Appeals—0
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