ARXIV:2603.02150 · NAMED-ENTITY RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

arXiv

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies. Named-Entity Recognition (NER) can perform this task in extracting information about the crime, the criminal, or law enforcement agencies…

METHOD

Full abstract

The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. Named-Entity Recognition (NER) can perform this task in extracting information about the crime, the criminal, or law enforcement agencies involved. However, there is a considerable lack of adequately annotated data on general real-world crime scenarios. To address this issue, we present CrimeNER, a case-study of Crime-related zero- and Few-Shot NER, and a general Crime-related Named-Entity Recognition database (CrimeNERdb) consisting of more than 1.5k annotated documents for the NER task extracted from public reports on terrorist attacks and the U.S. Department of Justice's press notes. We define 5 types of coarse crime entity and a total of 22 types of fine-grained entity. We address the quality of the case-study and the annotated data with experiments on Zero and Few-Shot settings with State-of-the-Art NER models as well as generalist and commonly used Large Language Models.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We address the quality of the case-study and the annotated data with experiments on Zero and Few-Shot settings with State-of-the-Art NER models as well…

WHY NOW

Named-Entity Recognition moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainCrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.02150 · NAMED-ENTITY RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

arXiv

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Named-Entity Recognition moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainCrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.02150

Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback40 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Named-Entity Recognition moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies. Named-Entity Recognition (NER) can perform this task in extracting information about the crime, the criminal, or law enforcement agencies involved.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. Named-Entity Recognition (NER) can perform this task in extracting information about the crime, the criminal, or law enforcement agencies involved.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. We address the quality of the case-study and the annotated data with experiments on Zero and Few-Shot settings with State-of-the-Art NER models as well as generalist and commonly used Large Language Models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Named-Entity Recognition moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Named-Entity Recognition

Competitors

not indexed

Competitive landscape

CrimeNER provides a specialized NER dataset for crime-related document analysis, useful for law enforcement agencies.

Segment

Named-Entity Recognition

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.02150 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(40)

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)

2025Gonzalo Mancera, A. Morales et al.

Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment

2025Alejandro Peña, Julian Fiérrez et al.

Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs

2025Miguel Lopez-Duran, Julian Fiérrez et al.

Knowledge Acquisition on Mass-shooting Events via LLMs for AI-Driven Justice

2025Benign John Ihugba, Afsana Nasrin et al.

SecLMNER: A framework for enhanced named entity recognition in multi-source cybersecurity data using large language models

2025Yunlong Zhang, Jingju Liu et al.

Overcoming Data Scarcity in Named Entity Recognition: Synthetic Data Generation with Large Language Models

2025An Dao, Hiroki Teranishi et al.

Hierarchical Multi-Class and Multi-Label Text Classification for Crime Report: A Traditional Machine Learning Approach

2025André R. Vieira, Glaucio De S. Santos et al.

Optimizing an LLM Prompt for Accurate Data Extraction from Firearm-Related Listings in Dark Web Marketplaces

2024Chaido Porlou, Maria Makrynioti et al.

BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition

2024Quanjiang Guo, Yihong Dong et al.

Multilingual Approaches to Named Entity Recognition

2024Padmaja Savaram, S. Tabassum et al.

Threat intelligence named entity recognition techniques based on few-shot learning

2024Haiyan Wang, Weimin Yang et al.

FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios

2024Yongjian Tang, Rakebul Hasan et al.

ColPali: Efficient Document Retrieval with Vision Language Models

2024Manuel Faysse, Hugues Sibille et al.

llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models

2024F. Villena, Luis Miranda et al.

Continuous document layout analysis: Human-in-the-loop AI-based data curation, database, and evaluation in the domain of public affairs

2024Alejandro Peña, A. Morales et al.

NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data

2024Sergei Bogdanov, Alexandre Constantin et al.

A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing

2023Paria Sarzaeim, Q. Mahmoud et al.

Few-shot Named Entity Recognition: Definition, Taxonomy and Research Directions

2023V. Moscato, Marco Postiglione et al.

From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts

2023Milos Kosprdic, Nikola Prodanović et al.

Showing 20 of 40 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisNoise-Robust Financial Numerical Entity Attribute Tagging

0.0

Builds On ThisOntology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reports

3.0

Commercially relevant

Higher ViabilitySAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition

7.0

Higher ViabilityIdentification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection

6.0

Higher ViabilityA Hybrid Method for Low-Resource Named Entity Recognition

7.0

Higher ViabilityNERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories

7.0

Higher ViabilityCV-18 NER: Augmented Common Voice for Named Entity Recognition from Arabic Speech

7.0

Higher ViabilityA Proposal-Free Query-Guided Network for Grounded Multimodal Named Entity Recognition

7.0

Higher ViabilityDiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

8.0

Higher ViabilityRECOVER: Robust Entity Correction via agentic Orchestration of hypothesis Variants for Evidence-based Recovery

7.0

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.02150
Route: /paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener
Active tab: read
Artifact: zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener/paper-pack
REST build passport API/api/v1/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "9fb47df0-30cd-4769-9f6e-3c07c3357eef",
  "arxiv_id": "2603.02150",
  "canonical_route": "/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener",
  "endpoints": {
    "paper_pack": "/api/v1/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener/paper-pack",
    "build_passport": "/api/v1/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

Canonical ID zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener | Route /paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.02150"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)",
  "normalized_query": "2603.02150",
  "route": "/paper/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener",
  "paper_ref": "zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

/buildability/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

Watchwatch

Subject: Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

Paper ref

zero-and-few-shot-named-entity-recognition-case-study-and-dataset-in-the-crime-domain-crimener

arXiv id

2603.02150

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

aa29a6e9fba9cba557ebd5137aa056ee258e48c6cd6927e93161e0b1e4ac6650

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

Zero- and Few-Shot Named-Entity Recognition: Case Study and Dataset in the Crime Domain (CrimeNER)

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