Parsing for agile modeling

Agile modeling refers to a set of methods that allow for a quick initial development of an importer and its further refinement. These requirements are not met simultaneously by the current parsing technology. Problems with parsing became a bottleneck in our research of agile modeling. In this thesis we introduce a novel approach to specify and build parsers. Our approach allows for expressive, tolerant and composable parsers without sacrificing performance. The approach is based on a context-sensitive extension of parsing expression grammars that allows a grammar engineer to specify complex language restrictions. To insure high parsing performance we automatically analyze a grammar definition and choose different parsing strategies for different parts of the grammar. We show that context-sensitive parsing expression grammars allow for highly composable, tolerant and variable-grained parsers that can be easily refined. Different parsing strategies significantly insure high-performance of parsers without sacrificing expressiveness of the underlying grammars

Multi-touch interaction for interface prototyping

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena

IMAGE MANAGEMENT USING PATTERN RECOGNITION SYSTEMS

With the popular usage of personal image devices and the continued increase of computing power, casual users need to handle a large number of images on computers. Image management is challenging because in addition to searching and browsing textual metadata, we also need to address two additional challenges. First, thumbnails, which are representative forms of original images, require significant screen space to be represented meaningfully. Second, while image metadata is crucial for managing images, creating metadata for images is expensive. My research on these issues is composed of three components which address these problems. First, I explore a new way of browsing a large number of images. I redesign and implement a zoomable image browser, PhotoMesa, which is capable of showing thousands of images clustered by metadata. Combined with its simple navigation strategy, the zoomable image environment allows users to scale up the size of an image collection they can comfortably browse. Second, I examine tradeoffs of displaying thumbnails in limited screen space. While bigger thumbnails use more screen space, smaller thumbnails are hard to recognize. I introduce an automatic thumbnail cropping algorithm based on a computer vision saliency model. The cropped thumbnails keep the core informative part and remove the less informative periphery. My user study shows that users performed visual searches more than 18% faster with cropped thumbnails. Finally, I explore semi-automatic annotation techniques to help users make accurate annotations with low effort. Automatic metadata extraction is typically fast but inaccurate while manual annotation is slow but accurate. I investigate techniques to combine these two approaches. My semi-automatic annotation prototype, SAPHARI, generates image clusters which facilitate efficient bulk annotation. For automatic clustering, I present hierarchical event clustering and clothing based human recognition. Experimental results demonstrate the effectiveness of the semi-automatic annotation when applied on personal photo collections. Users were able to make annotation 49% and 6% faster with the semi-automatic annotation interface on event and face tasks, respectively

Adaptive Analysis and Processing of Structured Multilingual Documents

Digital document processing is becoming popular for application to office and library automation, bank and postal services, publishing houses and communication management. In recent years, the demand for tools capable of searching written and spoken sources of multilingual information has increased tremendously, where the bilingual dictionary is one of the important resource to provide the required information. Processing and analysis of bilingual dictionaries brought up the challenges of dealing with many different scripts, some of which are unknown to the designer. A framework is presented to adaptively analyze and process structured multilingual documents, where adaptability is applied to every step. The proposed framework involves: (1) General word-level script identification using Gabor filter. (2) Font classification using the grating cell operator. (3) General word-level style identification using Gaussian mixture model. (4) An adaptable Hindi OCR based on generalized Hausdorff image comparison. (5) Retargetable OCR with automatic training sample creation and its applications to different scripts. (6) Bootstrapping entry segmentation, which segments each page into functional entries for parsing. Experimental results working on different scripts, such as Chinese, Korean, Arabic, Devanagari, and Khmer, demonstrate that the proposed framework can save human efforts significantly by making each phase adaptive