https://github.com/roccotrip/antisem
공식 코드Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914
Reproducing and learning new algebraic operations on word embeddings using genetic programming
Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
Quantifying Lexical Semantic Shift via Unbalanced Optimal Transport
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
QuanTaxo: A Quantum Approach to Self-Supervised Taxonomy Expansion
An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction
Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers
Follow the Leader: Documents on the Leading Edge of Semantic Change Get More Citations
Characterizing Diseases from Unstructured Text: A Vocabulary Driven Word2vec Approach
InfiniteWalk: Deep Network Embeddings as Laplacian Embeddings with a Nonlinearity
An Embedded Diachronic Sense Change Model with a Case Study from Ancient Greek
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection
RAW-C: Relatedness of Ambiguous Words--in Context (A New Lexical Resource for English)
Deep Text Mining of Instagram Data Without Strong Supervision
Combining Textual Features for the Detection of Hateful and Offensive Language
Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings
Evaluating Biased Attitude Associations of Language Models in an Intersectional Context
Zero-Shot Learning in Named-Entity Recognition with External Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.