Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts

Social Network Analysis (SNA) of organizations can attract great interest from government agencies and scientists for its ability to boost translational research and accelerate the process of converting research to care. For SNA of a particular disease area, we need to identify the key research groups in that area by mining the affiliation information from PubMed. This not only involves recognizing the organization names in the affiliation string, but also resolving ambiguities to identify the article with a unique organization. We present here a process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. We demonstrate the application of the method by analyzing organizations involved in angiogenensis treatment, and demonstrating the utility of the results for researchers in the pharmaceutical and biotechnology industries or national funding agencies.Comment: This paper has been withdrawn; First International Workshop on Graph Techniques for Biomedical Networks in Conjunction with IEEE International Conference on Bioinformatics and Biomedicine, Washington D.C., USA, Nov. 1-4, 2009; http://www.public.asu.edu/~sjonnal3/home/papers/IEEE%20BIBM%202009.pd

A Study of Style Effects on OCR Errors in the MEDLINE Database

The National Library of Medicine has developed a system for the automatic extraction of data from scanned journal articles to populate the MEDLINE database. Although the 5-engine OCR system used in this process exhibits good performance overall, it does make errors in character recognition that must be corrected in order for the process to achieve the requisite accuracy. The correction process works by feeding words that have characters with less than 100% confidence (as determined automatically by the OCR engine) to a human operator who then must manually verify the word or correct the error. The majority of these errors are contained in the affiliation information zone where the characters are in italics or small fonts. Therefore only affiliation information data is used in this research. This paper examines the correlation between OCR errors and various character attributes in the MEDLINE database, such as font size, italics, bold, etc. and OCR confidence levels. The motivation for this research is that if a correlation between the character style and types of errors exists it should be possible to use this information to improve operator productivity by increasing the probability that the correct word option is presented to the human editor. We have determined that this correlation exists, in particular for the case of characters with diacritics

Batch-adaptive rejection threshold estimation with application to OCR post-processing

An OCR process is often followed by the application of a language model to find the best transformation of an OCR hypothesis into a string compatible with the constraints of the document, field or item under consideration. The cost of this transformation can be taken as a confidence value and compared to a threshold to decide if a string is accepted as correct or rejected in order to satisfy the need for bounding the error rate of the system. Widespread tools like ROC, precision-recall, or error-reject curves, are commonly used along with fixed thresholding in order to achieve that goal. However, those methodologies fail when a test sample has a confidence distribution that differs from the one of the sample used to train the system, which is a very frequent case in post-processed OCR strings (e.g., string batches showing particularly careful handwriting styles in contrast to free styles). In this paper, we propose an adaptive method for the automatic estimation of the rejection threshold that overcomes this drawback, allowing the operator to define an expected error rate within the set of accepted (non-rejected) strings of a complete batch of documents (as opposed to trying to establish or control the probability of error of a single string), regardless of its confidence distribution. The operator (expert) is assumed to know the error rate that can be acceptable to the user of the resulting data. The proposed system transforms that knowledge into a suitable rejection threshold. The approach is based on the estimation of an expected error vs. transformation cost distribution. First, a model predicting the probability of a cost to arise from an erroneously transcribed string is computed from a sample of supervised OCR hypotheses. Then, given a test sample, a cumulative error vs. cost curve is computed and used to automatically set the appropriate threshold that meets the user-defined error rate on the overall sample. The results of experiments on batches coming from different writing styles show very accurate error rate estimations where fixed thresholding clearly fails. An original procedure to generate distorted strings from a given language is also proposed and tested, which allows the use of the presented method in tasks where no real supervised OCR hypotheses are available to train the system.Navarro Cerdan, JR.; Arlandis Navarro, JF.; Llobet Azpitarte, R.; Perez-Cortes, J. (2015). Batch-adaptive rejection threshold estimation with application to OCR post-processing. Expert Systems with Applications. 42(21):8111-8122. doi:10.1016/j.eswa.2015.06.022S81118122422

Learning to detect video events from zero or very few video examples

In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a textual description of the event, without using any positive video examples, and ii) additionally exploiting very few positive training samples together with a small number of ``related'' videos. For learning only from an event's textual description, we first identify a general learning framework and then study the impact of different design choices for various stages of this framework. For additionally learning from example videos, when true positive training samples are scarce, we employ an extension of the Support Vector Machine that allows us to exploit ``related'' event videos by automatically introducing different weights for subsets of the videos in the overall training set. Experimental evaluations performed on the large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness of the proposed methods.Comment: Image and Vision Computing Journal, Elsevier, 2015, accepted for publicatio

Toward the Optimized Crowdsourcing Strategy for OCR Post-Correction

Digitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using Optical Character Recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. This study investigates how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives. A series of experiments with different micro-task's structures and text lengths was conducted with 753 workers on the Amazon's Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures have been devised. The analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two-phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image. The study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction. This is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.Comment: 25 pages, 12 figures, 1 tabl

Crowdsourcing and Scholarly Culture: Understanding Expertise in an Age of Popularism

The increasing volume of digital material available to the humanities creates clear potential for crowdsourcing. However, tasks in the digital humanities typically do not satisfy the standard requirement for decomposition into microtasks each of which must require little expertise on behalf of the worker and little context of the broader task. Instead, humanities tasks require scholarly knowledge to perform and even where sub-tasks can be extracted, these often involve broader context of the document or corpus from which they are extracted. That is the tasks are macrotasks, resisting simple decomposition. Building on a case study from musicology, the In Concert project, we will explore both the barriers to crowdsourcing in the creation of digital corpora and also examples where elements of automatic processing or less-expert work are possible in a broader matrix that also includes expert microtasks and macrotasks. Crucially we will see that the macrotask–microtask distinction is nuanced: it is often possible to create a partial decomposition into less-expert microtasks with residual expert macrotasks, and crucially do this in ways that preserve scholarly values

Crowdsourcing and Scholarly Culture: Understanding Expertise in an Age of Popularism

The increasing volume of digital material available to the humanities creates clear potential for crowdsourcing. However, tasks in the digital humanities typically do not satisfy the standard requirement for decomposition into microtasks each of which must require little expertise on behalf of the worker and little context of the broader task. Instead, humanities tasks require scholarly knowledge to perform and even where sub-tasks can be extracted, these often involve broader context of the document or corpus from which they are extracted. That is the tasks are macrotasks, resisting simple decomposition. Building on a case study from musicology, the In Concert project, we will explore both the barriers to crowdsourcing in the creation of digital corpora and also examples where elements of automatic processing or less-expert work are possible in a broader matrix that also includes expert microtasks and macrotasks. Crucially we will see that the macrotask–microtask distinction is nuanced: it is often possible to create a partial decomposition into less-expert microtasks with residual expert macrotasks, and crucially do this in ways that preserve scholarly values

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information