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Paper

BiLSTM-enhanced legal text extraction model using fuzzy logic and metaphor recognition

by Independent / Community 02eb3ff713ac33eb0fb43d32ca5bce2663c95943

Free2AITools Nexus Index

64.7

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 76

P: Popularity 51

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

The burgeoning field of natural language processing (NLP) has witnessed exponential growth, captivating researchers due to its diverse practical applications across industries. However, the intricate nature of legal texts poses unique challenges for conventional text extraction methods. To surmount these challenges, this article introduces a pioneering legal text extraction model rooted in fuzzy language processing and metaphor recognition, tailored for the domain of online environment govern...

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Semantic Scholar 10 Citations

Paper Information Summary
Entity Passport
Registry ID	02eb3ff713ac33eb0fb43d32ca5bce2663c95943
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{02eb3ff713ac33eb0fb43d32ca5bce2663c95943,
  author = {Unknown},
  title = {BiLSTM-enhanced legal text extraction model using fuzzy logic and metaphor recognition Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/02eb3ff713ac33eb0fb43d32ca5bce2663c95943}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). BiLSTM-enhanced legal text extraction model using fuzzy logic and metaphor recognition [Paper]. Free2AITools. https://api.semanticscholar.org/02eb3ff713ac33eb0fb43d32ca5bce2663c95943

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 76

Popularity (P) 51

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for BiLSTM-enhanced legal text extraction model using fuzzy logic and metaphor recognition: Authority (A:76), Popularity (P:51), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"The burgeoning field of natural language processing (NLP) has witnessed exponential growth, captivating researchers due to its diverse practical applications across industries. However, the intricate nature of legal texts poses unique challenges for conventional text extraction methods. To surmount these challenges, this article introduces a pioneering legal text extraction model rooted in fuzzy language processing and metaphor recognition, tailored for the domain of online environment govern..."

❝ Cite Node

@article{Unknown2026BiLSTM-enhanced,
  title={BiLSTM-enhanced legal text extraction model using fuzzy logic and metaphor recognition},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈10CitationsSemantic Scholar

🏛️76AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

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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
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 10

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