An Approach for Process Model Extraction by Multi-grained Text Classification

Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shu’ang Li, Jianmin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations


Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions. Previous process extractors heavily depend on manual features and ignore the potential relations between clues of different text granularities. In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without manually-defined procedural features. Under this structure, we accordingly propose the coarse-to-fine (grained) learning mechanism, training multi-grained tasks in coarse-to-fine grained order to share the high-level knowledge for the low-level tasks. To evaluate our approach, we construct two multi-grained datasets from two different domains and conduct extensive experiments from different dimensions. The experimental results demonstrate that our approach outperforms the state-of-the-art methods with statistical significance and further investigations demonstrate its effectiveness.

Original languageEnglish (US)
Title of host publicationAdvanced Information Systems Engineering - 32nd International Conference, CAiSE 2020, Proceedings
EditorsSchahram Dustdar, Eric Yu, Vik Pant, Camille Salinesi, Dominique Rieu
Number of pages15
ISBN (Print)9783030494346
StatePublished - 2020
Event32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020 - Grenoble, France
Duration: Jun 8 2020Jun 12 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12127 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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