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Paper Citation Record · LEDGER

Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2003.07082.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2003.07082 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:46:41.693007Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fdb0146f-b305-44df-8301-19bf05e65c3a · inbound

HuggingFace's Transformers: State-of-the-art Natural Language Processing cites this paper.

HuggingFace's Transformers: State-of-the-art Natural Language Processing Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 177

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:53:59.813577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-11T14:53:58.963468Z digest=sha256:89065e9673dd0418592be06b72e4532b96e429c29578ab409ade1a146a132d7d

Observation 213d288c-62ad-464c-a708-530c35a4b8d7 · inbound

FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language cites this paper.

FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-06T22:46:41.693007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:46:41.693007Z digest=sha256:8bce6cd0cba4db137c4074d84c85544b241499a5f6dd40674e3369942e59065a

Observation 00d34718-879b-4a9b-b75d-12643fe64e54 · inbound

Verified Language Processing with Hybrid Explainability: A Technical Report cites this paper.

Verified Language Processing with Hybrid Explainability: A Technical Report Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T19:40:08.478976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:40:08.478976Z digest=sha256:31165be0b12a9ee3775e7a38c55a6504f0b8eca963200a5b7dadbf28d22231b4

Observation b54bd189-56c8-4223-9302-983b7954b2b0 · inbound

RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records cites this paper.

RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-19T03:57:02.892601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T03:56:39.732945Z digest=sha256:20d51e40f0515fdb29feeba8f3ce0cf5420482bd73837b4dfdd6cf4a7edea4db

Observation 7024cd76-2a0d-4eac-b776-c3a1ede5c2a1 · inbound

ROVI: A VLM-LLM Re-Captioned Dataset for Open-Vocabulary Instance-Grounded Text-to-Image Generation cites this paper.

ROVI: A VLM-LLM Re-Captioned Dataset for Open-Vocabulary Instance-Grounded Text-to-Image Generation Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-06T05:59:15.577307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:59:15.577307Z digest=sha256:57caa0ea1967c43ef7941e58f46c80f0265a74aabcb2cc894616b98c3df44d56

Observation 6f64dd79-dd13-4aa9-8e38-938f8c73e991 · inbound

Enhancing User-Feedback Driven Requirements Prioritization cites this paper.

Enhancing User-Feedback Driven Requirements Prioritization Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-04T05:41:28.138682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:41:28.138682Z digest=sha256:3de02f31d25cfa195d26ead32f170aa4b3030a2fc5e2dc12d5a1adb97981d4fd

Observation 039b5968-8ca1-4652-b6f8-7ea7fb0fd891 · inbound

Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection cites this paper.

Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-10T15:40:34.592246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:35:36.783582Z digest=sha256:ae6f36cbdd7a43a1470b0e56bc464344360da6d2cd68bb8f6e389e32949d4c31

Observation e5847d92-bfea-4090-bc17-bd8a2c8cf169 · inbound

The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text cites this paper.

The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:06:28.254265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-07T07:58:44.632993Z digest=sha256:ef01c1e995df6464487e6fad5d284eb3cff4e09164457f489cbf70c4cefc3c4e

Observation 012520e9-91ea-4232-beec-d057fdc20fd4 · inbound

Methods, Data, and Conceptual Change: Reflections from Two Quantitative Diachronic Case Studies cites this paper.

Methods, Data, and Conceptual Change: Reflections from Two Quantitative Diachronic Case Studies Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-08T19:29:05.193593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T19:24:41.226711Z digest=sha256:88a6d6cf7ab5ae5dc0e95e39091b3f149b4acbbfd0caa233ecbc16a727243553

Observation 17681080-84a8-4349-b2b1-504d8fb574c6 · inbound

Parser agreement and disagreement in L2 Korean UD: Implications for human-in-the-loop annotation cites this paper.

Parser agreement and disagreement in L2 Korean UD: Implications for human-in-the-loop annotation Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:11:12.577452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-08T09:58:22.185992Z digest=sha256:1c6ead83fc4a3a783569c7cb1fa5bd3059ed632a2372a510a90377fc849f07aa

Observation 809ffd61-7647-4e45-97f4-c41ef4457d6d · inbound

Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt cites this paper.

Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 190

Resolution
verified exact
arxiv_id, observed 2026-07-01T23:36:22.766274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T14:13:13.648560Z digest=sha256:8856e694f4d28a4ee9ab72d4a1285e1a7c66d322aebce7bf6486176971aea190

Observation b637cf9b-bd2d-4aa8-b13d-efa47a49cfea · inbound

Do It Right! A Methodology for Successful NLP System Development cites this paper.

Do It Right! A Methodology for Successful NLP System Development Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 33

Resolution
unresolved
no resolver link, observed 2026-07-11T04:25:29.271082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T04:25:29.271082Z digest=sha256:fb25302af0b535b73b49baa0e10c27e21b8a9f7d8b6f39b1801e11f6075961e5

Observation dc4fe401-1259-49af-bddd-8f2ad0c1c56f · inbound

Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution cites this paper.

Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution Stanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-02T14:50:22.053224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T14:50:22.053224Z digest=sha256:322ab1629eec063633d5c45e11867f48dc3b65f4e871e280f7cb97731cad4bb6