The General Data Protection Regulation (GDPR) poses enormous challenges on companies and organizations with respect to understanding, implementing, and maintaining the contained constraints. We report on how the ConRelMiner method can be used for untangling the GDPR. For this, the GDPR is filtered and grouped along the roles mentioned by the GDPR and the reduction of sentences to be read by analysts is shown. Moreover, the output of the ConRelMiner - a cluster graph with relations between the sentences - is displayed and interpreted. Overall the goal is to illustrate how the effort for implementing the GDPR can be reduced and a structured and meaningful representation of the relevant GDPR sentences can be found.
Extracting knowledge from text is a broad and highly regarded task in science and practice. One distinction is the type of text or documents that is analyzed. It ranges from social media texts, e.g., @cite_1 over regulatory documents, e.g., @cite_0 @cite_4 @cite_3 @cite_8 and business process descriptions, e.g., @cite_6 @cite_2 @cite_11 to historic text analysis as in digital humanities, e.g., @cite_7 .
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One of the important Natural Language Processing applications is Text Summarization, which helps users to manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included. Text Summarization can be classified according to the type of summary: extractive, and abstractive. Extractive summary is the procedure of identifying important sections of the text and producing them verbatim while abstractive summary aims to produce important material in a new generalized form. In this paper, a novel approach is presented to create an abstractive summary for a single document using a rich semantic graph reducing technique. The approach summaries the input document by creating a rich semantic graph for the original document, reducing the generated graph, and then generating the abstractive summary from the reduced graph. Besides, a simulated case study is presented to show how the original text was minimized to fifty percent. Information extraction in specialized texts raises different problems related to the kind of searched information. In this paper, we are interested in relation identification between some concepts in medical reports, task that was evaluated in the i2b2 2010 challenge. As relations are expressed in natural language with a great variety of forms, we proceeded to sentence analysis by extracting features that enable all together to identify a relation and we modeled this task as a multi-class classification based on an SVM, each type of relation representing a class. We will present the selection of the features used by our system and an error analysis. This approach allowed us to obtain an F-measure of 0.70, classifying the system among the best systems. The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback. This article proposes a process to retrieve the URL of a document for which metadata records exist in a digital library catalog but a pointer to the full text of the document is not available. The process uses results from queries submitted to Web search engines for finding the URL of the corresponding full text or any related material. We present a comprehensive study of this process in different situations by investigating different query strategies applied to three general purpose search engines (Google, Yahoo!, MSN) and two specialized ones (Scholar and CiteSeer), considering five user scenarios. Specifically, we have conducted experiments with metadata records taken from the Brazilian Digital Library of Computing (BDBComp) and The DBLP Computer Science Bibliography (DBLP). We found that Scholar was the most effective search engine for this task in all considered scenarios and that simple strategies for combining and re-ranking results from Scholar and Google significantly improve the retrieval quality. Moreover, we study the influence of the number of query results on the effectiveness of finding missing information as well as the coverage of the proposed scenarios. The goal of information extraction is to extract database records from text or semi-structured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computationally efficient, this approach is suboptimal, because it ignores the fact that segmenting one candidate record can help to segment similar ones. For example, resolving a well-segmented field with a less-clear one can disambiguate the latter's boundaries. In this paper we propose a joint approach to information extraction, where segmentation of all records and entity resolution are performed together in a single integrated inference process. While a number of previous authors have taken steps in this direction (eg., (2003), (2004)), to our knowledge this is the first fully joint approach. In experiments on the CiteSeer and Cora citation matching datasets, joint inference improved accuracy, and our approach outperformed previous ones. Further, by using Markov logic and the existing algorithms for it, our solution consisted mainly of writing the appropriate logical formulas, and required much less engineering than previous ones. Capturing knowledge from free-form evaluative texts about an entity is a challenging task. New techniques of feature extraction, polarity determination and strength evaluation have been proposed. Feature extraction is particularly important to the task as it provides the underpinnings of the extracted knowledge. The work in this paper introduces an improved method for feature extraction that draws on an existing unsupervised method. By including user-specific prior knowledge of the evaluated entity, we turn the task of feature extraction into one of term similarity by mapping crude (learned) features into a user-defined taxonomy of the entity's features. Results show promise both in terms of the accuracy of the mapping as well as the reduction in the semantic redundancy of crude features. Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present a system, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER employs a bottom-up learning algorithm which incorporates techniques from several inductive logic programming systems and acquires unbounded patterns that include constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains. This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method. Existing research on analyzing information graphics assume to have a perfect text detection and extraction available. However, text extraction from information graphics is far from solved. To fill this gap, we propose a novel processing pipeline for multi-oriented text extraction from infographics. The pipeline applies a combination of data mining and computer vision techniques to identify text elements, cluster them into text lines, compute their orientation, and uses a state-of-the-art open source OCR engine to perform the text recognition. We evaluate our method on 121 infographics extracted from an open access corpus of scientific publications. The results show that our approach is effective and significantly outperforms a state-of-the-art baseline.