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Paper

A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction

by Independent / Community 0034b308f348051d7ae89ce54b8e5afb381aefd9

Free2AITools Nexus Index

66.7

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 79

P: Popularity 54

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

As a major method for relation extraction, distantly supervised relation extraction (DSRE) suffered from the noisy label problem and class imbalance problem (these two problems are also common for many other NLP tasks, e.g., text classification). However, there seems no existing research in DSRE or other NLP tasks that can simultaneously solve both problems, which is a significant insufficiency in related researches. In this paper, we propose a loss function which is robust to noisy label and...

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Semantic Scholar 16 Citations

Paper Information Summary
Entity Passport
Registry ID	0034b308f348051d7ae89ce54b8e5afb381aefd9
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0034b308f348051d7ae89ce54b8e5afb381aefd9,
  author = {Unknown},
  title = {A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0034b308f348051d7ae89ce54b8e5afb381aefd9}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction [Paper]. Free2AITools. https://api.semanticscholar.org/0034b308f348051d7ae89ce54b8e5afb381aefd9

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 79

Popularity (P) 54

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction: Authority (A:79), Popularity (P:54), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"As a major method for relation extraction, distantly supervised relation extraction (DSRE) suffered from the noisy label problem and class imbalance problem (these two problems are also common for many other NLP tasks, e.g., text classification). However, there seems no existing research in DSRE or other NLP tasks that can simultaneously solve both problems, which is a significant insufficiency in related researches. In this paper, we propose a loss function which is robust to noisy label and..."

❝ Cite Node

@article{Unknown2026A,
  title={A noisy label and negative sample robust loss function for DNN-based distant supervised relation extraction},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈16CitationsSemantic Scholar

🏛️79AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 16

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