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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.
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Towards Fair and Explainable AI using a Human-Centered AI Approach
Alzheimer Disease Classification through ASR-based Transcriptions: Exploring the Impact of Punctuation and Pauses
Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations
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Word Embeddings for Banking Industry
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Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation
Research on Multilingual News Clustering Based on Cross-Language Word Embeddings
A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces
Backpack Language Models
Language Models Implement Simple Word2Vec-style Vector Arithmetic
Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages
ChatGPT-EDSS: Empathetic Dialogue Speech Synthesis Trained from ChatGPT-derived Context Word Embeddings
Probing Brain Context-Sensitivity with Masked-Attention Generation
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation
Detecting and Mitigating Indirect Stereotypes in Word Embeddings
Word Embeddings Are Steers for Language Models
Measuring Intersectional Biases in Historical Documents
A quantitative study of NLP approaches to question difficulty estimation
Controllable Speaking Styles Using a Large Language Model
CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models
Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance
Multi-Relational Hyperbolic Word Embeddings from Natural Language Definitions
IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data
Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?
Semantic Frame Induction with Deep Metric Learning
Analyzing Vietnamese Legal Questions Using Deep Neural Networks with Biaffine Classifiers
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