region:us
Abstract. Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work we aim at filling this gap and establishing the first steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization that is aimed from Business Process Management. For this, we develop the first corpus of business process descriptions annotated with activities, gateways, actors and flow information. We present our new resource, including a detailed overview of the annotation schema and guidelines, as well as a variety of baselines to benchmark the difficulty and challenges of business process extraction from text.
@inproceedings{DBLP:conf/bpm/BellanADGP22,
  author       = {Patrizio Bellan and
                  Han van der Aa and
                  Mauro Dragoni and
                  Chiara Ghidini and
                  Simone Paolo Ponzetto},
  editor       = {Cristina Cabanillas and
                  Niels Frederik Garmann{-}Johnsen and
                  Agnes Koschmider},
  title        = {{PETv11:} An Annotated Dataset for Process Extraction from Natural Language
                  Text Tasks},
  booktitle    = {Business Process Management Workshops - {BPM} 2022 International Workshops,
                  M{\"{u}}nster, Germany, September 11-16, 2022, Revised Selected
                  Papers},
  series       = {Lecture Notes in Business Information Processing},
  volume       = {460},
  pages        = {315--321},
  publisher    = {Springer},
  year         = {2022},
  url          = {https://doi.org/10.1007/978-3-031-25383-6\_23},
  doi          = {10.1007/978-3-031-25383-6\_23},
  timestamp    = {Tue, 14 Feb 2023 09:47:10 +0100},
  biburl       = {https://dblp.org/rec/conf/bpm/BellanADGP22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/aiia/BellanGDPA22,
  author       = {Patrizio Bellan and
                  Chiara Ghidini and
                  Mauro Dragoni and
                  Simone Paolo Ponzetto and
                  Han van der Aa},
  editor       = {Debora Nozza and
                  Lucia C. Passaro and
                  Marco Polignano},
  title        = {Process Extraction from Natural Language Text: the {PETv11} Dataset and
                  Annotation Guidelines},
  booktitle    = {Proceedings of the Sixth Workshop on Natural Language for Artificial
                  Intelligence {(NL4AI} 2022) co-located with 21th International Conference
                  of the Italian Association for Artificial Intelligence (AI*IA 2022),
                  Udine, November 30th, 2022},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3287},
  pages        = {177--191},
  publisher    = {CEUR-WS.org},
  year         = {2022},
  url          = {https://ceur-ws.org/Vol-3287/paper18.pdf},
  timestamp    = {Fri, 10 Mar 2023 16:23:01 +0100},
  biburl       = {https://dblp.org/rec/conf/aiia/BellanGDPA22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
---
dataset_info:
  features:
  - name: document name
    dtype: string
  - name: tokens
    sequence: string
  - name: tokens-IDs
    sequence: int8
  - name: ner_tags
    sequence: string
  - name: sentence-IDs
    sequence: int8
  - name: relations
    sequence:
    - name: source-head-sentence-ID
      dtype: int8
    - name: source-head-word-ID
      dtype: int8
    - name: relation-type
      dtype: string
    - name: target-head-sentence-ID
      dtype: int8
    - name: target-head-word-ID
      dtype: int8
  splits:
  - name: test
    num_bytes: 203379
    num_examples: 45
  download_size: 38326
  dataset_size: 203379
---

This is the version 1.1.0 of the original PET dataset.
in this version we fixed ``the Performs Relations'' and few minor errors.

Please refer to the original [PET Dataset repository](https://huggingface.co/datasets/patriziobellan/PET) for more info.