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ARXIV:2604.02171 · NLP COREFERENCE RESOLUTION · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.02171NLP COREFERENCE RESOLUTIONSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALEAtilla Kaan Alkan · Felix Grezes · Jennifer Lynn Bartlett · Anna Kelbert · Kelly Lockhart · Alberto Accomazzi · arXiv
A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora.
Opportunity summary
Pain A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora. We compare two fine-tuning-free approaches: Fuzzy Matching (FM), a lexical string-similarity method,…
We present our participation in the SOMD 2026 shared task on cross-document software mention coreference resolution, where our systems ranked second across all three subtasks. We compare two fine-tuning-free approaches: Fuzzy Matching (FM), a…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Both achieve competitive performance across all subtasks (CoNLL F1 of 0.94-0.96), with CAR consistently outperforming FM by 1 point on the official test set,…
NLP Coreference Resolution moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora.
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10.48550/arXiv.2604.02171A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora.
Abstract
We present our participation in the SOMD 2026 shared task on cross-document software mention coreference resolution, where our systems ranked second across all three subtasks. We compare two fine-tuning-free approaches: Fuzzy Matching (FM), a lexical string-similarity method, and Context Aware Representations (CAR), which combines mention-level and document-level embeddings. Both achieve competitive performance across all subtasks (CoNLL F1 of 0.94-0.96), with CAR consistently outperforming FM by 1 point on the official test set, consistent with the high surface regularity of software names, which reduces the need for complex semantic reasoning. A controlled noise-injection study reveals complementary failure modes: as boundary noise increases, CAR loses only 0.07 F1 points from clean to fully corrupted input, compared to 0.20 for FM, whereas under mention substitution, FM degrades more gracefully (0.52 vs. 0.63). Our inference-time analysis shows that FM scales superlinearly with corpus size, whereas CAR scales approximately linearly, making CAR the more efficient choice at large scale. These findings suggest that system selection should be informed by both the noise profile of the upstream mention detector and the scale of the target corpus. We release our code to support future work on this underexplored task.
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PROBLEM
A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora. We compare two fine-tuning-free approaches: Fuzzy Matching (FM), a lexical string-similarity method, and Context A...
METHOD
We present our participation in the SOMD 2026 shared task on cross-document software mention coreference resolution, where our systems ranked second across all three subtasks. We compare two fine-tuning-free approaches: Fuzzy Matching (FM), a lexical string-similarity method, an...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Both achieve competitive performance across all subtasks (CoNLL F1 of 0.94-0.96), with CAR consistently outperforming FM by 1 point on the official test set, consistent with the high surface regularity of...
WHY NOW
NLP Coreference Resolution moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
with CAR consistently outperforming FM by 1 point on the official test set
Directly stated in the abstract with specific performance comparison.
partial
as boundary noise increases, CAR loses only 0.07 F1 points from clean to fully corrupted input, compared to 0.20 for FM
Directly stated in the abstract with clear numeric evidence.
partial
under mention substitution, FM degrades more gracefully (0.52 vs. 0.63)
Directly stated in the abstract with clear numeric evidence.
partial
FM scales superlinearly with corpus size, whereas CAR scales approximately linearly
Directly stated in the abstract with clear comparison, though exact scaling factors not provided.
partial
Both achieve competitive performance across all subtasks (CoNLL F1 of 0.94-0.96)
Directly stated in the abstract with specific performance range.
partial
consistent with the high surface regularity of software names, which reduces the need for complex semantic reasoning
Directly stated in the abstract as an explanation for performance patterns.
partial
These findings suggest that system selection should be informed by both the noise profile of the upstream mention detector and the scale of the target corpus
Directly stated in the abstract as a conclusion from the empirical findings.
partial
making CAR the more efficient choice at large scale
Strongly implied in the abstract by comparing scaling properties and stating CAR is more efficient at large scale.
partial
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A novel system for robust software mention coreference resolution that outperforms existing methods under noisy conditions and scales efficiently for large corpora.
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NLP Coreference Resolution
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