https://github.com/pdasigi/onto-lstm
공식 코드Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
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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.
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Analytical Methods for Interpretable Ultradense Word Embeddings
Magnitude: A Fast, Efficient Universal Vector Embedding Utility Package
ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-training
SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization
Recovering Private Text in Federated Learning of Language Models
Multi-label Categorization of Accounts of Sexism using a Neural Framework
Understanding Linearity of Cross-Lingual Word Embedding Mappings
CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
Emotion-Aware Transformer Encoder for Empathetic Dialogue Generation
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation
CTRAN: CNN-Transformer-based Network for Natural Language Understanding
"Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language
Multimodal Hate Speech Detection from Bengali Memes and Texts
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
Language Embeddings Sometimes Contain Typological Generalizations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.