Document Embedding
23 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Latest papers
ARAGOG: Advanced RAG Output Grading
Sentence Window Retrieval emerged as the most effective for retrieval precision, despite its variable performance on answer similarity.
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
rooooyy/hill
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Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information.
Approach to Predicting News -- A Precise Multi-LSTM Network With BERT
According to the V-Dem annual democracy report 2019, Taiwan is one of the two countries that got disseminated false information from foreign governments the most.
BERTopic: Neural topic modeling with a class-based TF-IDF procedure
BERTopic generates coherent topics and remains competitive across a variety of benchmarks involving classical models and those that follow the more recent clustering approach of topic modeling.
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
malteos/scincl
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Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics.
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
gzerveas/CODER
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Contrastive learning has been the dominant approach to training dense retrieval models.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
linhanz/mderank
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Findings (ACL) 2022
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
xnliang98/uke_ccrank
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EMNLP 2021
In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices.
Multifaceted Domain-Specific Document Embeddings
philipphager/faceted-domain-encoder
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NAACL 2021
Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms.
Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
