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Word Embeddings
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes
Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change
Ontology Enhanced Claim Detection
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings
Word Embeddings Revisited: Do LLMs Offer Something New?
Injecting Wiktionary to improve token-level contextual representations using contrastive learning
Semi-Supervised Learning for Bilingual Lexicon Induction
Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
Layer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing
Graph-based Clustering for Detecting Semantic Change Across Time and Languages
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs
Multi-class Regret Detection in Hindi Devanagari Script
Pre-training and Diagnosing Knowledge Base Completion Models
Semantic Properties of cosine based bias scores for word embeddings
CERM: Context-aware Literature-based Discovery via Sentiment Analysis
Expressivity-aware Music Performance Retrieval using Mid-level Perceptual Features and Emotion Word Embeddings
Contrastive Learning in Distilled Models
Multilingual acoustic word embeddings for zero-resource languages
GWPT: A Green Word-Embedding-based POS Tagger
Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research
Estimating Text Similarity based on Semantic Concept Embeddings
MoSECroT: Model Stitching with Static Word Embeddings for Crosslingual Zero-shot Transfer
An Analysis of Embedding Layers and Similarity Scores using Siamese Neural Networks
Effect of dimensionality change on the bias of word embeddings
Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling
Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations
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