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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.
Strong and weak alignment of large language models with human values
Representation Bias of Adolescents in AI: A Bilingual, Bicultural Study
ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science
Misinforming LLMs: vulnerabilities, challenges and opportunities
Ontological Relations from Word Embeddings
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction
You shall know a piece by the company it keeps. Chess plays as a data for word2vec models
The BIAS Detection Framework: Bias Detection in Word Embeddings and Language Models for European Languages
On Initializing Transformers with Pre-trained Embeddings
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models
WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning
Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changes
Deep Image-to-Recipe Translation
Spiking Convolutional Neural Networks for Text Classification
MT2ST: Adaptive Multi-Task to Single-Task Learning
CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation
Evaluating Contextualized Representations of (Spanish) Ambiguous Words: A New Lexical Resource and Empirical Analysis
In-Context Former: Lightning-fast Compressing Context for Large Language Model
Statistical Uncertainty in Word Embeddings: GloVe-V
Exploring Intra and Inter-language Consistency in Embeddings with ICA
Multimodal Representation Loss Between Timed Text and Audio for Regularized Speech Separation
Understanding Visual Concepts Across Models
Automated Trustworthiness Testing for Machine Learning Classifiers
Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings
Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels
Synergizing Unsupervised and Supervised Learning: A Hybrid Approach for Accurate Natural Language Task Modeling
What Are Large Language Models Mapping to in the Brain? A Case Against Over-Reliance on Brain Scores
An Automatic Question Usability Evaluation Toolkit
FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
fMRI predictors based on language models of increasing complexity recover brain left lateralization
Crossmodal ASR Error Correction with Discrete Speech Units
DefSent+: Improving sentence embeddings of language models by projecting definition sentences into a quasi-isotropic or isotropic vector space of unlimited dictionary entries
Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Grounding
E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems
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