# An Approach for Process Model Extraction By Multi-Grained Text Classification

## TL;DR

This paper introduces a hierarchical neural network approach for process model extraction from text, leveraging multi-grained classification to improve accuracy without manual feature engineering.

## Contribution

It formalizes process model extraction as a multi-grained classification problem and proposes a coarse-to-fine learning mechanism with a hierarchical neural network.

## Key findings

- Outperforms state-of-the-art methods with statistical significance
- Effective in modeling multi-grained textual information
- Validated on datasets from two different domains

## Abstract

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.