Multilevel Text Research Articles

Das Signalisieren von Kohärenzstrukturen in deutschsprachigen wissenschaftlichen Texten

This paper discusses the relation between newer approaches of intercultural rhetoric research (and specifically its distinction between “big” and “small” cultures) and the academic literacies approach which assumes that local institutional and individual biographic factors influence decisively the development of an individual academic writing competence. Based on this discussion it claims that the “research article” does not constitute a single genre but rather a genre colony consisting of members (genres) which differ according to linguistic, disciplinary, institutional and individual affiliations and status of writers and which are connected through a system of family resemblances. This claim is exemplified through a qualitative case study of two German academic texts, a “professional” published paper and a students’ seminar paper. By applying a multi-level text analysis combining genre analysis and rhetorical structure analysis, it is shown that although the two texts exhibit several similarities in terms of their genre structure, their rhetorical macro- and micro-structures differ significantly. These results are interpreted as a corollary of the different institutional and hierarchical positions of the authors and as a single-case support for the initial claim of the paper.

English

The textual pieces in scene images might often provide vital semantic data for visual content understanding, indexing and analysis; as a result, text extraction had become a significant research area in image processing and computer vision. In this paper, we propose a new hybrid multilevel algorithm to extract text in various scene images. The algorithm converts the Red – Green –Blue (RGB) image into grayscale for color reduction. Next, it applies edge detection and mathematical morphological operations to extract edges in the image preprocessing phase. The resultant binary image passes through three subsequent levels in a multi layer behavior. Connected components labeling and text candidates' selection take place in each level through different criteria analysis. We used the structural features of connected components as basis criteria for selecting candidate texts, those features include: area, width, length and condense intensity mean of connected components. Afterwards, Horizontal projection profile analysis is used to further refine the candidate text areas and to eliminate non-text regions. The proposed algorithm is evaluated on a set of fifty images chosen from a well known text locating test dataset: KAIST. Extensive experiments show high robustness under different environments such as indoor, outdoor, shadow, night and light, and for different text properties such as various font size, style and complexities of backgrounds and textures. The algorithm effectively extracts textual contents from scenes images with high average of Precision, Recall, and F-Score which are 90.1, 99, and 94.3%, respectively.    Key words: Multilevel text extraction, hybrid text extraction, edge detection, connected components, text candidates, morphological operations, horizontal projection profile. &nbsp

Study on Multi-level Text Clustering for Knowledge Base Based on Domain Ontology——Taking Knowledge Base of Chinese Cuisine Culture as an Example

Study on Multi-level Text Clustering for Knowledge Base Based on Domain Ontology——Taking Knowledge Base of Chinese Cuisine Culture as an Example

Virtual Reality: The Effects and Phenomenon of Sign

Abstract The problems of virtual reality assume a special actuality nowadays in the culture and in the philosophical approaches. The modern philosophy is firmly connected with the linguistic and sign problematic. The virtual reality is analyzed as special space and time, in which ontological and gnosiological problems acquire a particular perception. The research of virtual reality as complicated and multilevel text, as the sign system, can fix different ontological layers. The analysis of virtual space and its certain phenomena (such as “phenomena of iconic presence”, “the symbolism of identification”, and “the effect of ontological insufficiency”) can be realized from semiotic and philosophy of sign position. The revelation of such phenomena can help to develop the approach of analysis of ontological problems, the problems of virtual space and time, the problems of cyberspace. Moreover, the semiotic analysis of social relations, which are being modified with the appearance of virtual space, can help us to describe the semiotic, phenomenology and pragmatics “I - We” relations.

Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection

The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload. We selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N(pos)=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations. Classifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed. Rule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively). Our validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.

Multilevel text mining for bone biology

SUMMARYOsteoporosis is characterized by reduced bone mass and debilitating fractures and is likely to reach epidemic proportions. Because of the vigorous research taking place in fields related to osteoporosis, bone biologists are overwhelmed by the amount of literature being generated on a regular basis. This problem can be alleviated by inferring and extracting novel relationships among biological entities appearing in the biological literature. With the development of large online publicly available databases of biological literature, such an approach becomes even more appealing. The novel relationships between biological terms thus discovered constitute new hypotheses that can be verified using experiments. This paper presents a novel method called multilevel text mining for the extraction of potentially meaningful biological relationships. Multilevel mining uses transitive maximum flow graph analysis coupled with set combination operations of union and intersection. Set operators are applied along and across the paths of a transitive flow graph to combine the data. In the first level of the multilevel mining process, protein domain names are used. Novel relationships between domains are extracted by the transitive text mining analysis. In the second level, these newly discovered relationships are used to extract relevant protein names. Set operators are used in various combinations to obtain different sets of results. Copyright © 2011 John Wiley & Sons, Ltd.

Exploring Collections of Tagged Text for Literary Scholarship

AbstractModern literary scholars must combine access to vast collections of text with the traditional close analysis of their field. In this paper, we discuss the design and development of tools to support this work. Based on analysis of the needs of literary scholars, we constructed a suite of visualization tools for the analysis of large collections of tagged text (i.e. text where one or more words have been annotated as belonging to a specific category). These tools unite the aspects of the scholars’ work: large scale overview tools help to identify corpus‐wide statistical patterns while fine scale analysis tools assist in finding specific details that support these observations. We designed visual tools that support and integrate these levels of analysis. The result is the first tool suite that can support the multilevel text analysis performed by scholars, combining standard visual elements with novel methods for selecting individual texts and identifying represenative passages in them.