Enhanced services for targeted information retrieval by event extraction and data mining

Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Question-answering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user's question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament. --

Enhanced Services for Targeted Information Retrieval by Event Extraction and Data Mining

Where Information Retrieval (IR) and Text Categorization delivers a set of (ranked) documents according to a query, users of large document collections would rather like to receive answers. Questionanswering from text has already been the goal of the Message Understanding Conferences. Since then, the task of text understanding has been reduced to several more tractable tasks, most prominently Named Entity Recognition (NER) and Relation Extraction. Now, pieces can be put together to form enhanced services added on an IR system. In this paper, we present a framework which combines standard IR with machine learning and (pre-)processing for NER in order to extract events from a large document collection. Some questions can already be answered by particular events. Other questions require an analysis of a set of events. Hence, the extracted events become input to another machine learning process which delivers the final output to the user’s question. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.

Innovationen in der Hochschulbildung: Massive Open Online Courses an den deutschen Hochschulen

Seit die Informatiker Sebastian Thrun und Peter Norvig an der Stanford University 2011 begannen, kostenlose, videobasierte Online-Kurse samt Tests, Prüfungen und Kommunikation in Foren ohne Teilnehmerbeschränkung anzubieten, die außergewöhnlich hohe Teilnehmerzahlen erreichten, ist in der Öffentlichkeit und an Hochschulen über Massive Open Online Courses (MOOCs) als ein neues Lehr- und Lernarrangement und eine Innovation in der Hochschulbildung diskutiert worden, die nicht nur gängige Formen der Hochschullehre, sondern das klassische Hochschulmodell an sich in Frage zu stellen schien. Vor dem Hintergrund der auch in Deutschland intensiv geführten Debatte um die Potenziale, Ausprägungen und Einsatzfelder für MOOCs ging HIS-Hochschulentwicklung in zwei Umfragen den Einschätzungen von Hochschulleitungen sowie von Lehrenden, die MOOCs anbieten bzw. dies planen, nach. Die Zielsetzung der Umfragen bestand darin, Aufschluss über die Bewertung der MOOC-Thematik durch die Präsidien und Rektorate deutscher Hochschulen zu geben und ergänzend Erfah-rungen der bislang noch überschaubaren Gruppe von Lehrenden deutscher Hochschulen, die im Bereich der Lehre mit MOOCs aktiv sind, abzubilden

Handling Tree-Structured Values in RapidMiner

Attribute value types play an important role in mostly every datamin- ing task. Most learners, for instance, are restricted to particular value types. The usage of such learners is just possible after special forms of preprocessing. RapidMiner most commonly distinguishes between nom- inal and numerical values which are well-known to every RapidMiner- user. Although, covering a great fraction of attribute types being present in nowadays datamining tasks, nominal and numerical attribute values are not sufficient for every type of feature. In this work we are focusing on attribute values containing a tree-structure. We are presenting the handling and especially the possibilities to use tree-structured data for modelling. Additionally, we are introducing particular tasks which are offering tree-structured data and might benefit from using those struc- tures for modelling. All methods presented in this paper are contained in the Information Extraction Plugin1 for RapidMiner

An algorithm for calculating the Lorentz angle in silicon detectors

Future experiments will use silicon sensors in the harsh radiation environment of the LHC (Large Hadron Collider) and high magnetic fields. The drift direction of the charge carriers is affected by the Lorentz force due to the high magnetic field. Also the resulting radiation damage changes the properties of the drift. In this paper measurements of the Lorentz angle of electrons and holes before and after irradiation are reviewed and compared with a simple algorithm to compute the Lorentz angle.Comment: 13 pages, 7 figures, final version accepted by NIMA. Mainly clarifications included and slightly shortene

Tree Kernel Usage in Naive Bayes Classifiers

We present a novel approach in machine learning by combining naive Bayes classifiers with tree kernels. Tree kernel methods produce promising results in machine learning tasks containing treestructured attribute values. These kernel methods are used to compare two tree-structured attribute values recursively. Up to now tree kernels are only used in kernel machines like Support Vector Machines or Perceptrons. In this paper, we show that tree kernels can be utilized in a naive Bayes classifier enabling the classifier to handle tree-structured values. We evaluate our approach on three datasets containing tree-structured values. We show that our approach using tree-structures delivers significantly better results in contrast to approaches using non-structured (flat) features extracted from the tree. Additionally, we show that our approach is significantly faster than comparable kernel machines in several settings which makes it more useful in resource-aware settings like mobile devices

Lorentz angle measurements in irradiated silicon detectors between 77 K and 300 K

Future experiments are using silicon detectors in a high radiation environment and in high magnetic fields. The radiation tolerance of silicon improves by cooling it to temperatures below 180 K. At low temperatures the mobility increases, which leads to larger deflections of the charge carriers by the Lorentz force. A good knowledge of the Lorentz angle is needed for design and operation of silicon detectors. We present measurements of the Lorentz angle between 77 K and 300 K before and after irradiation with a primary beam of 21 MeV protons.Comment: 13 pages, 9 figures, submitted to ICHEP2000, Osaka, Japa