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        <title>Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management</title>
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    <abstract>Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One of the most crucial tasks of BPM is discovering and modelling business processes from text documents. In this paper, we present our system that resolves an end-to-end problem consisting of 1) recognizing conditional sentences from technical documents, 2) finding boundaries to extract conditional and resultant clauses from each conditional sentence, and 3) categorizing resultant clause as Action or Consequence which later helps to generate new steps in our business process model automatically. We created a new dataset and three models to solve this problem. Our best model achieved very promising results of 83.82, 87.84, and 85.75 for Precision, Recall, and F1, respectively, for extracting Condition, Action, and Consequence clauses using Exact Match metric.</abstract>
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%0 Conference Proceedings
%T Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management
%A Vo, Ngoc Phuoc An
%A Manotas, Irene
%A Popescu, Octavian
%A Černiauskas, Algimantas
%A Sheinin, Vadim
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F vo-etal-2021-recognizing
%X Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One of the most crucial tasks of BPM is discovering and modelling business processes from text documents. In this paper, we present our system that resolves an end-to-end problem consisting of 1) recognizing conditional sentences from technical documents, 2) finding boundaries to extract conditional and resultant clauses from each conditional sentence, and 3) categorizing resultant clause as Action or Consequence which later helps to generate new steps in our business process model automatically. We created a new dataset and three models to solve this problem. Our best model achieved very promising results of 83.82, 87.84, and 85.75 for Precision, Recall, and F1, respectively, for extracting Condition, Action, and Consequence clauses using Exact Match metric.
%U https://aclanthology.org/2021.ranlp-1.167/
%P 1490-1497

Markdown (Informal)

[Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management](https://aclanthology.org/2021.ranlp-1.167/) (Vo et al., RANLP 2021)

• Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management (Vo et al., RANLP 2021)

ACL

• Ngoc Phuoc An Vo, Irene Manotas, Octavian Popescu, Algimantas Černiauskas, and Vadim Sheinin. 2021. Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1490–1497, Held Online. INCOMA Ltd..