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ARXIV:2603.24246 · NLP COREFERENCE RESOLUTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24246NLP COREFERENCE RESOLUTIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEJulia Matela · Frank Krüger · arXiv
A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering.
Opportunity summary
Pain A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora.
This paper describes the system submitted to the SOMD 2026 Shared Task for Cross-Document Coreference Resolution (CDCR) of software mentions. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Surface-form normalization and abbreviation resolution are applied to improve canonical name matching.
NLP Coreference Resolution moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering.
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10.48550/arXiv.2603.24246A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering.
Abstract
This paper describes the system submitted to the SOMD 2026 Shared Task for Cross-Document Coreference Resolution (CDCR) of software mentions. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora. We propose a hybrid framework that combines dense semantic embeddings from a pre-trained Sentence-BERT model, Knowledge Base (KB) lookup strategy built from training-set cluster centroids using FAISS for efficient retrieval, and HDBSCAN density-based clustering for mentions that cannot be confidently assigned to existing clusters. Surface-form normalization and abbreviation resolution are applied to improve canonical name matching. The same core pipeline is applied to Subtasks 1 and 2. To address the large scale settings of Subtask 3, the pipeline was adapted by utilising a blocking strategy based on entity types and canonicalized surface forms. Our system achieved CoNLL F1 scores of 0.98, 0.98, and 0.96 on Subtasks 1, 2, and 3 respectively.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Time to MVP
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Dimensions overall score 4.0
PROBLEM
A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora.
METHOD
This paper describes the system submitted to the SOMD 2026 Shared Task for Cross-Document Coreference Resolution (CDCR) of software mentions. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Surface-form normalization and abbreviation resolution are applied to improve canonical name matching.
WHY NOW
NLP Coreference Resolution moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This paper describes the system submitted to the SOMD 2026 Shared Task for Cross-Document Coreference Resolution (CDCR) of software mentions. Our approach addresses the challenge of identifying and clustering inconsistent software mentions across scientific corpora.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Surface-form normalization and abbreviation resolution are applied to improve canonical name matching.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
NLP Coreference Resolution moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A system for resolving inconsistent software mentions across scientific documents using semantic embeddings and density-based clustering.
Segment
NLP Coreference Resolution
Adoption evidence
No public code link in the paper record yet
Commercial read
4.0/10 public viability
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status
missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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No budget owner is verified for this paper.
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Defensibility
missing
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Defensibility signals are missing.
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
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No CRM or outreach source attached.
People
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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BUZZ
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