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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This paper describes new methods of automatically extracting documents for screening purposes, i.e. the computer selection of sentences having the greatest potential for conveying to the reader the substance of the document. While previous work has focused on one component of sentence significance, namely, the presence of high-frequency content words (key words), the methods described here also treat three additional components: pragmatic words (cue words); title and heading words; and structural indicators (sentence location). The research has resulted in an operating system and a research methodology. The extracting system is parameterized to control and vary the influence of the above four components. The research methodology includes procedures for the compilation of the required dictionaries, the setting of the control parameters, and the comparative evaluation of the automatic extracts with manually produced extracts. The results indicate that the three newly proposed components dominate the frequency component in the production of better extracts. Extracting constraints and process models from natural language text is an ongoing challenge. While the focus of current research is merely on the extraction itself, this paper presents a three step approach to group constraints as well as to detect and display relations between constraints in order to ease their implementation. For this, the approach uses NLP techniques to extract sentences containing constraints, group them by, e.g., stakeholders or topics, and detect redundant, subsuming, and conflicting pairs of constraints. These relations are displayed using network maps. The approach is prototypically implemented and evaluated based on regulatory documents from the financial sector as well as expert interviews. Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods. This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar and the Link parser. The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.