Text Preprocessing in Programmable Logic

There is a tremendous amount of information being generated and stored every year, and its growth rate is exponential. From 2008 to 2009, the growth rate was estimated to be 62%. In 2010, the amount of generated information is expected to grow by 50% to 1.2 Zettabytes, and by 2020 this rate is expected to grow to 35 Zettabytes. By preprocessing text in programmable logic, high data processing rates could be achieved with greater power efficiency than with an equivalent software solution, leading to a smaller carbon footprint. This thesis presents an overview of the fields of Information Retrieval and Natural Language Processing, and the design and implementation of four text preprocessing modules in programmable logic: UTF–8 decoding, stop–word filtering, and stemming with both Lovins’ and Porter’s techniques. These extensively pipelined circuits were implemented in a high performance FPGA and found to sustain maximum operational frequencies of 704 MHz, data throughputs in excess of 5 Gbps and efficiencies in the range of 4.332 – 6.765 mW/Gbps and 34.66 – 108.2 uW/MHz. These circuits can be incorporated into larger systems, such as document classifiers and information extraction engines

Gigabit Concept Mining: A Sensitivity Analysis, Masters Thesis, December 2006

Massive amounts of data are passed over public networks. There is a need for network administrators to analyze this traffic, but it was not previously possible to analyze live network data at high speed. It has been shown that streaming computation and deep packet analysis are possible at very high rates through the use of hardware acceleration. This work provides analysis for a larger project that involves digesting large amounts of network traffic. In this system, we process the traffic using hardware that has constraints. The workings of the system are first discussed. Tradeoffs in the design of hardware and software components are also discussed. Next, an experiment to classify topics of newsgroups is described that utilizes the system. The contribution of this thesis is to show that it is possible to change the parameters of the system to minimize the representation of concepts

Antennas and Electromagnetics Research via Natural Language Processing.

Advanced techniques for performing natural language processing (NLP) are being utilised to devise a pioneering methodology for collecting and analysing data derived from scientific literature. Despite significant advancements in automated database generation and analysis within the domains of material chemistry and physics, the implementation of NLP techniques in the realms of metamaterial discovery, antenna design, and wireless communications remains at its early stages. This thesis proposes several novel approaches to advance research in material science. Firstly, an NLP method has been developed to automatically extract keywords from large-scale unstructured texts in the area of metamaterial research. This enables the uncovering of trends and relationships between keywords, facilitating the establishment of future research directions. Additionally, a trained neural network model based on the encoder-decoder Long Short-Term Memory (LSTM) architecture has been developed to predict future research directions and provide insights into the influence of metamaterials research. This model lays the groundwork for developing a research roadmap of metamaterials. Furthermore, a novel weighting system has been designed to evaluate article attributes in antenna and propagation research, enabling more accurate assessments of impact of each scientific publication. This approach goes beyond conventional numeric metrics to produce more meaningful predictions. Secondly, a framework has been proposed to leverage text summarisation, one of the primary NLP tasks, to enhance the quality of scientific reviews. It has been applied to review recent development of antennas and propagation for body-centric wireless communications, and the validation has been made available for comparison with well-referenced datasets for text summarisation. Lastly, the effectiveness of automated database building in the domain of tunable materials and their properties has been presented. The collected database will use as an input for training a surrogate machine learning model in an iterative active learning cycle. This model will be utilised to facilitate high-throughput material processing, with the ultimate goal of discovering novel materials exhibiting high tunability. The approaches proposed in this thesis will help to accelerate the discovery of new materials and enhance their applications in antennas, which has the potential to transform electromagnetic material research

Interim research assessment 2003-2005 - Computer Science

This report primarily serves as a source of information for the 2007 Interim Research Assessment Committee for Computer Science at the three technical universities in the Netherlands. The report also provides information for others interested in our research activities

Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences

This book is the product of an international workshop dedicated to addressing data accessibility in the linguistics field. It is therefore vital to the book’s mission that its content be open access. Linguistics as a field remains behind many others as far as data management and accessibility strategies. The problem is particularly acute in the subfield of language acquisition, where international linguistic sound files are needed for reference. Linguists' concerns are very much tied to amount of information accumulated by individual researchers over the years that remains fragmented and inaccessible to the larger community. These concerns are shared by other fields, but linguistics to date has seen few efforts at addressing them. This collection, undertaken by a range of leading experts in the field, represents a big step forward. Its international scope and interdisciplinary combination of scholars/librarians/data consultants will provide an important contribution to the field

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters