Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions

Mirco Pyrtek; Philip Hake1; and Peter Loos

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions", 
							Author= "Mirco Pyrtek, Philip Hake1, and Peter Loos", 
							Doi= "https://doi.org/10.30844/wi_2020_r11-pyrtek", 
							 Abstract= "Recently, Artificial Neural Networks (ANN) have shown high potential in the area of Natural Language Processing (NLP). In the area of sentence compression, the application of ANNs has proven to outperform existing rule-based approaches. Nevertheless, these approaches require a decent amount of training data to achieve high accuracy. In this work, we aim at employing ANNs to derive process model labels from process descriptions. Since the amount of publicly available pairs of text and process model is scarce, we employ a transfer learning approach. While training the compression model on a large corpus consisting of sentence-compression pairs, we transfer the model to the problem of deriving label descriptions. We implement our approach and conduct an experimental evaluation using pairs of process descriptions and models. We found that our transfer learning model keeps high recall while losing performance on precision and compression rate.

", 
							 Keywords= "Business Process Modeling, Deep Leaning, Sentence Compression, Artificial Neural Network, Natural Language Processing
", 
							}

Mirco Pyrtek, Philip Hake1, and Peter Loos: Using Artificial Neural Networks to Derive Process Model Activity Labels from Process Descriptions. Online: https://doi.org/10.30844/wi_2020_r11-pyrtek (Abgerufen 25.04.24)

Abstract

Recently, Artificial Neural Networks (ANN) have shown high potential in the area of Natural Language Processing (NLP). In the area of sentence compression, the application of ANNs has proven to outperform existing rule-based approaches. Nevertheless, these approaches require a decent amount of training data to achieve high accuracy. In this work, we aim at employing ANNs to derive process model labels from process descriptions. Since the amount of publicly available pairs of text and process model is scarce, we employ a transfer learning approach. While training the compression model on a large corpus consisting of sentence-compression pairs, we transfer the model to the problem of deriving label descriptions. We implement our approach and conduct an experimental evaluation using pairs of process descriptions and models. We found that our transfer learning model keeps high recall while losing performance on precision and compression rate.

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