Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

A bioinformatics knowledge discovery in text application for grid computing

<p>Abstract</p> <p>Background</p> <p>A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources.</p> <p>Methods</p> <p>The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs.</p> <p>Results</p> <p>A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed.</p> <p>Conclusion</p> <p>In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities.</p

Understanding complex constructions: a quantitative corpus-linguistic approach to the processing of english relative clauses

Die vorliegende Arbeit präsentiert einen korpusbasierten Ansatz an die kognitive Verarbeitung komplexer linguistische Konstruktionen am Beispiel englischer Relativsatzkonstruktionen (RCC). Im theoretischen Teil wird für eine konstruktionsgrammatische Perspektive auf sprachliches Wissen argumentiert, welche erlaubt, RCCs als schematische Konstruktionen zu charakterisieren. Diese Perspektive wird mit Konzeptionen exemplarbasierter Modelle menschlicher Sprachverarbeitung zusammengeführt, welche die Verarbeitung einer linguistischen Struktur als Funktion von der Häufigkeit vergangener Verarbeitungen typidentischer Vorkommnisse begreift. Häufige Strukturen gelangen demnach zu einem priviligierten Status im kognitiven System eines Sprechers, welcher in konstruktionsgrammatischen Theorien als entrenchment bezeichnet wird. Während der jeweilge entrenchment-Wert einer gegebenen Konstruktion für konkrete Zeichen vergleichsweise einfach zu bestimmen ist, wird die Einschätzung mit ansteigender Komplexität und Schematizität der Zielkonstruktion zunehmend schwieriger. Für höherstufige N-gramme, welche durch eine grosse Anzahl an variablen Positionen ausgezeichnet sind, ist das Feld noch vergleichweise unerforscht. Die vorliegende Arbeit ist bemüht, diese Lücke zu schließen entwickelt eine korpusbasierte mehrstufige Messprozedur, um den entrenchment-Wert komplexer schematischer Konstruktionen zu erfassen. Da linguistisches Wissen hochstrukturiert ist und menschliche Sprachverarbeitungsprozesse struktursensitiv sind, wird ein clusteranalytisches Verfahren angewendet, welches die salienten RCC hinsichtlich ihrer strukturellen Ähnlichlichkeit organisiert. Aus der Position einer RCC im konstruktionalen Netzwerk sowie dessen entrenchment-Wert kann nun der Grad der erwarteteten Verarbeitungsschwierigkeit abgeleitet werden. Der abschliessende Teil der Arbeit interpretiert die Ergebnisse vor dem Hintergrung psycholinguistischer Befunde zur Relativsatzverarbeitung