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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.
Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments
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The flow of ideas in word embeddings
Towards Resolving Word Ambiguity with Word Embeddings
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
Self-Supervised Acoustic Word Embedding Learning via Correspondence Transformer Encoder
A Topical Approach to Capturing Customer Insight In Social Media
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach
Bidirectional Attention as a Mixture of Continuous Word Experts
Evaluating Biased Attitude Associations of Language Models in an Intersectional Context
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages
Leveraging multilingual transfer for unsupervised semantic acoustic word embeddings
Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings
Racial Bias Trends in the Text of US Legal Opinions
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Interpretable Neural Embeddings with Sparse Self-Representation
Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs
Semi-automated extraction of research topics and trends from NCI funding in radiological sciences from 2000-2020
Meta-Personalizing Vision-Language Models to Find Named Instances in Video
A Bayesian approach to uncertainty in word embedding bias estimation
Contrastive Loss is All You Need to Recover Analogies as Parallel Lines
"Definition Modeling: To model definitions." Generating Definitions With Little to No Semantics
Does mBERT understand Romansh? Evaluating word embeddings using word alignment
Curatr: A Platform for Semantic Analysis and Curation of Historical Literary Texts
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