Process graph extraction (PGE) is a recently emerged interdiscipline between natural language processing and business process management, which aims to extract process graphs expressed in texts. 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 PGE task into the multi-granularity text classification problem, and propose a hierarchical model to effectively model and extract multi-granularity information without manually defined procedural knowledge. Under this framework, we accordingly propose the coarse-to-fine learning mechanism, training multi-granularity tasks in coarse-to-fine order to share the high-level knowledge for the low-level tasks. To evaluate our approach, we construct two finer-grained datasets from two sentence-level corpora 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 the ablation studies demonstrate its effectiveness.
Several methods have been originally applied to process extraction @cite_19 @cite_8 @cite_15 @cite_5 . They employ many NLP systems or tools such as GATE @cite_8 @cite_15 and Stanford Parser @cite_19 @cite_5 to label each sentence and extract main actions and resources. Although they are easy to develop and interpret, they require a large number of linguistic rules created by domain experts.
@cite_19 @cite_15 @cite_5 @cite_8
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Procedural knowledge is the knowledge required to perform certain tasks, and forms an important part of expertise. A major source of procedural knowledge is natural language instructions. While these readable instructions have been useful learning resources for human, they are not interpretable by machines. Automatically acquiring procedural knowledge in machine interpretable formats from instructions has become an increasingly popular research topic due to their potential applications in process automation. However, it has been insufficiently addressed. This paper presents an approach and an implemented system to assist users to automatically acquire procedural knowledge in structured forms from instructions. We introduce a generic semantic representation of procedures for analysing instructions, using which natural language techniques are applied to automatically extract structured procedures from instructions. The method is evaluated in three domains to justify the generality of the proposed semantic representation as well as the effectiveness of the implemented automatic system. User generated Web content includes large amounts of procedural knowledge (also called how to knowledge). This paper is on a comparison of two extraction methods for procedural knowledge from the Web. Both methods create workflow representations automatically from text with the aim to reuse the Web experience by reasoning methods. Two variants of the workflow extraction process are introduced and evaluated by experiments with cooking recipes as a sample domain. The first variant is a term-based approach that integrates standard information extraction methods from the GATE system. The second variant is a frame-based approach that is implemented by means of the SUNDANCE system. The expert assessment of the extraction results clearly shows that the more sophisticated frame-based approach outperforms the term-based approach of automated workflow extraction. The increasing adoption of process-aware information systems (PAISs) has resulted in large process model collections. To support users having different perspectives on complex processes and related data, a PAIS should enable personalized process views, i.e., user-specific abstractions of process models. Despite the abstraction achieved through views of the graphical process models, many end users still struggle with understanding these graphical models and their details. For selected user groups, therefore, a PAIS should provide verbalized process descriptions describing their role in the process. Existing PAISs neither provide mechanisms for managing process views nor verbalized process descriptions. While process views have been used as visual abstractions for large process models, so far no work exists on how to provide both personalized and verbalized process descriptions based on respective views. This paper presents an approach for creating such personalized and verbalized process descriptions based on process views. Furthermore, textual changes of a personalized and verbalized process description are correctly mapped to corresponding updates of the underlying process model. In this context, all other views and process descriptions related to this process model are migrated to the new version of the process model as well. Overall, our approach enables end users to understand and evolve large process models based on personalized and verbalized process descriptions.