Ein Modell zur Repräsentation von Nachrichtentypen

In diesem Papier stellen wir einen Formalismus vor, mit dem eine computergerechte Repräsentation verschiedener Klassen von Geschäftsbriefen möglich ist. Der Ausgangspunkt dieser Bemühungen ist das Projekt ALV (Automatisches Lesen und Verstehen), dessen Ziel das partielle Erkennen einer eingeschränkten Menge von Geschäftsbriefen ist. Für die verschiedenen Klassen von Geschäftsbriefen werden sogenannte Nachrichtentypen entwickelt, die sich aus einzelnen Bausteinen, den Nachrichtenelementen zusammensetzen. Diese werden durch eine modifizierte Conceptual Dependency-Notation definiert. Durch die hierarchische und modulare Definition kann eine breite Anzahl von Geschäftsbriefen modelliert werden. Die Zielrichtung des hier vorgestellten Modells liegt neben der effizienten Modellierung der Nachrichtentypen in der Bildbearbeitung durch ein Verfahren zur erwartungsgesteuerten Textanalyse in ALV. Um dies zu ermöglichen, wurden zahlreiche Steuerungselemente in das Nachrichtenmodell aufgenommen

Ein erwartungsgesteuerter Koordinator zur partiellen Textanalyse

In dieser Papier wird die koordinierende Komponente eines Systems zur erwartungsgesteuerten Textanalyse auf der eingeschränkten Domäne deutscher Geschäftsbriefdokumente vorgestellt: Dazu wurden wesentliche Konzepte und Datenstrukturen zur Modellierung der Domäne, das Nachrichtenmodell, entwickelt (siehe [Gores & Bleisinger 92]). Mit diesem Nachrichtenmodell steuert die Komponente die Textextraktion der Informationen eines vorliegenden Briefdokumentes. Sie wird in ihrer Arbeit von Spezialisten, sogenannten Substantiierern, unterstützt, die auf dem Text arbeiten. Dazu muß intensiver Nutzen von den Informationen eines Lexikons gemacht werden. Die Repräsentation des Ergebnisses erfolgt in einer Form, die eine weitere Verarbeitung, wie die semantische Interpretation und eine darauf aufbauende Generierung neuer Aktionen begünstigt

Text skimming as a part in paper document understanding

In our document understanding project ALV we analyse incoming paper mail in the domain of single-sided German business letters. These letters are scanned and after several analysis steps the text is recognized. The result may contain gaps, word alternatives, and even illegal words. The subject of this paper is the subsequent phase which concerns the extraction of important information predefined in our "message type model". An expectation driven partial text skimming analysis is proposed focussing on the kernel module, the so-called "predictor". In contrast to traditional text skimming the following aspects are important in our approach. Basically, the input data are fragmentary texts. Rather than having one text analysis module ("substantiator") only, our predictor controls a set of different and partially alternative substantiators. With respect to the usually proposed three working phases of a predictor - start, discrimination, and instantiation - the following differences are remarkable. The starting problem of text skimming is solved by applying specialized substantiators for classifying a business letter into message types. In order to select appropriate expectations within the message type hypotheses a twofold discrimination is performed. A coarse discrimination reduces the number of message type alternatives, and a fine discrimination chooses one expectation within one or a few previously selected message types. According to the expectation selected substantiators are activated. Several rules are applied both for the verification of the substantiator results and for error recovery if the results are insufficient

Text skimming as a part in paper document understanding

In our document understanding project ALV we analyse incoming paper mail in the domain of single-sided German business letters. These letters are scanned and after several analysis steps the text is recognized. The result may contain gaps, word alternatives, and even illegal words. The subject of this paper is the subsequent phase which concerns the extraction of important information predefined in our "message type model". An expectation driven partial text skimming analysis is proposed focussing on the kernel module, the so-called "predictor". In contrast to traditional text skimming the following aspects are important in our approach. Basically, the input data are fragmentary texts. Rather than having one text analysis module ("substantiator") only, our predictor controls a set of different and partially alternative substantiators. With respect to the usually proposed three working phases of a predictor - start, discrimination, and instantiation - the following differences are remarkable. The starting problem of text skimming is solved by applying specialized substantiators for classifying a business letter into message types. In order to select appropriate expectations within the message type hypotheses a twofold discrimination is performed. A coarse discrimination reduces the number of message type alternatives, and a fine discrimination chooses one expectation within one or a few previously selected message types. According to the expectation selected substantiators are activated. Several rules are applied both for the verification of the substantiator results and for error recovery if the results are insufficient

Genomic investigations of unexplained acute hepatitis in children

Since its first identification in Scotland, over 1,000 cases of unexplained paediatric hepatitis in children have been reported worldwide, including 278 cases in the UK1. Here we report an investigation of 38 cases, 66 age-matched immunocompetent controls and 21 immunocompromised comparator participants, using a combination of genomic, transcriptomic, proteomic and immunohistochemical methods. We detected high levels of adeno-associated virus 2 (AAV2) DNA in the liver, blood, plasma or stool from 27 of 28 cases. We found low levels of adenovirus (HAdV) and human herpesvirus 6B (HHV-6B) in 23 of 31 and 16 of 23, respectively, of the cases tested. By contrast, AAV2 was infrequently detected and at low titre in the blood or the liver from control children with HAdV, even when profoundly immunosuppressed. AAV2, HAdV and HHV-6 phylogeny excluded the emergence of novel strains in cases. Histological analyses of explanted livers showed enrichment for T cells and B lineage cells. Proteomic comparison of liver tissue from cases and healthy controls identified increased expression of HLA class 2, immunoglobulin variable regions and complement proteins. HAdV and AAV2 proteins were not detected in the livers. Instead, we identified AAV2 DNA complexes reflecting both HAdV-mediated and HHV-6B-mediated replication. We hypothesize that high levels of abnormal AAV2 replication products aided by HAdV and, in severe cases, HHV-6B may have triggered immune-mediated hepatic disease in genetically and immunologically predisposed children