Paper Citation Record · LEDGER

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text

As of 11 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 2 inbound Pith citation observations for arXiv:2507.08362.

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

pith.paper-citation-record.v1
2507.08362 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:27:20.751647Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:55:52.809862Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:06:01.230798Z

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy22
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 953ca4ad-674d-4569-bab7-a2bc04ded1bf · outbound

This paper cites Automated generation of business process models from natural language input,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Automated generation of business process models from natural language input,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:24.090076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ec950562-f083-4161-87d7-737daf71b5b4 · outbound

This paper cites (2014) About the business process model and notation specification version 2.0.2.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text (2014) About the business process model and notation specification version 2.0.2

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.917942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4a7913f9-6b85-4a8c-b95d-c8d09957c863 · outbound

This paper cites Beyond rule-based named entity recognition and relation extraction for process model generation from natural language text,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Beyond rule-based named entity recognition and relation extraction for process model generation from natural language text,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.839819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3d25977-c89a-4b4b-855f-2cb16da21db8 · outbound

This paper cites Process model generation from natural language text,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Process model generation from natural language text,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.739585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0a4f0392-0548-48eb-a64b-693bec51afe0 · outbound

This paper cites Extracting business process entities and relations from text using pre-trained language models and in-context learning,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Extracting business process entities and relations from text using pre-trained language models and in-context learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.650873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 86fd1140-6912-49d1-9126-273c5ced8a0d · outbound

This paper cites Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:19.343269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9366fa1-2d70-4603-812c-96e493f049d4 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:19.430968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77c74524-a7fd-4a3b-8046-1b091e4a7768 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:19.543027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 792dc07f-6d93-4581-abce-9851b1b0af25 · outbound

This paper cites A universal prompting strategy for extracting process model information from natural language text using large language models,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text A universal prompting strategy for extracting process model information from natural language text using large language models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.560346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9f1fedde-3158-46b9-88f1-7c52d942d08e · outbound

This paper cites Large language models can accomplish business process management tasks,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Large language models can accomplish business process management tasks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.489758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9de9db54-95fc-40dd-a88c-4afedb427446 · outbound

This paper cites Process modeling with large language models,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Process modeling with large language models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.385657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 696428c1-6a15-4e4f-a821-f17a651e385c · outbound

This paper cites PET: an annotated dataset for process extraction from natural language text tasks,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text PET: an annotated dataset for process extraction from natural language text tasks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.281949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8413505a-c8a6-4226-8866-30ae155e4959 · outbound

This paper cites A comprehensive investigation of bpmn models generation from textual requirements—techniques, tools and trends,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text A comprehensive investigation of bpmn models generation from textual requirements—techniques, tools and trends,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.176146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 554ecc0d-5195-412d-b935-27b66fb024af · outbound

This paper cites Information extraction,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Information extraction,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:23.085270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3f10c3ff-76c1-4258-b520-161038da7093 · outbound

This paper cites Catboost: unbiased boosting with categorical features,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Catboost: unbiased boosting with categorical features,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.984694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0b144965-943e-48a0-b244-ee834fcd7c7d · outbound

This paper cites A survey on deep learning for named entity recognition,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text A survey on deep learning for named entity recognition,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.856133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8ffec7cc-cc3d-4c80-bccf-0a9155216df3 · outbound

This paper cites Conditional random fields: Probabilistic models for segmenting and labeling sequence data,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Conditional random fields: Probabilistic models for segmenting and labeling sequence data,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.698637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 64b7a607-c1d6-49a5-9e49-6c7df800f14f · outbound

This paper cites How to fine-tune bert for text classification?.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text How to fine-tune bert for text classification?

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.576809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71e97988-c60c-469b-8517-a10287a2128d · outbound

This paper cites Conditional random fields: An introduction,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Conditional random fields: An introduction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.466473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1095c5bf-85c8-42b1-98bf-2a601671bb01 · outbound

This paper cites Text chunking using transformation-based learning,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Text chunking using transformation-based learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.239600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0a745538-35aa-4cc6-9dc8-6d65b20f4507 · outbound

This paper cites Seven process modeling guidelines (7pmg),.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Seven process modeling guidelines (7pmg),

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:22.076511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2abc0361-a6da-4743-be6f-6b0d51b1ed55 · outbound

This paper cites Aconceptforgeneratingbusinessprocessmodels from natural language description,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Aconceptforgeneratingbusinessprocessmodels from natural language description,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:21.732639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c062332-5e49-4e9d-8e24-c0837c76f177 · outbound

This paper cites Glove: Global vectors for word representation,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Glove: Global vectors for word representation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:21.472340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 10bfefb5-971c-406d-bc31-0d52a6bb6b08 · outbound

This paper cites Smote: Synthetic minority over-sampling technique,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text Smote: Synthetic minority over-sampling technique,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:21.215319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 278272b7-cbfa-42f1-a5fa-a541f47e7b17 · outbound

This paper cites How much language is enough? theoretical and practical use of the business process modeling notation,.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text How much language is enough? theoretical and practical use of the business process modeling notation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:27:20.980546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9bd6e89e-2d7d-44c6-9d8e-4c57d51c1a67 · outbound

This paper cites How to Fine-Tune BERT for Text Classification?.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text How to Fine-Tune BERT for Text Classification?

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T18:27:20.374992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation eb7928ae-ac75-48c2-b10a-9875ac254a34 · inbound

Automatic Generation of Executable BPMN Models from Medical Guidelines cites this paper.

Automatic Generation of Executable BPMN Models from Medical Guidelines Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:51:02.514250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 51cb1e9d-3e02-4a6a-946a-6b8db9dfa76d · inbound

Automated BPMN Model Generation from Textual Process Descriptions: A Multi-Stage LLM-Driven Approach cites this paper.

Automated BPMN Model Generation from Textual Process Descriptions: A Multi-Stage LLM-Driven Approach Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:06:01.242848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.