Semi-Automated Text Classification

There is currently a high demand for information systems that automatically analyze textual data, since many organizations, both private and public, need to process large amounts of such data as part of their daily routine, an activity that cannot be performed by means of human work only. One of the answers to this need is text classification (TC), the task of automatically labelling textual documents from a domain D with thematic categories from a predefined set C. Modern text classification systems have reached high efficiency standards, but cannot always guarantee the labelling accuracy that applications demand. When the level of accuracy that can be obtained is insufficient, one may revert to processes in which classification is performed via a combination of automated activity and human effort. One such process is semi-automated text classification (SATC), which we define as the task of ranking a set D of automatically labelled textual documents in such a way that, if a human annotator validates (i.e., inspects and corrects where appropriate) the documents in a top-ranked portion of D with the goal of increasing the overall labelling accuracy of D, the expected such increase is maximized. An obvious strategy is to rank D so that the documents that the classifier has labelled with the lowest confidence are top-ranked. In this dissertation we show that this strategy is suboptimal. We develop new utility-theoretic ranking methods based on the notion of validation gain, defined as the improvement in classification effectiveness that would derive by validating a given automatically labelled document. We also propose new effectiveness measures for SATC-oriented ranking methods, based on the expected reduction in classification error brought about by partially validating a ranked list generated by a given ranking method. We report the results of experiments showing that, with respect to the baseline method above, and according to the proposed measures, our utility-theoretic ranking methods can achieve substantially higher expected reductions in classification error. We therefore explore the task of SATC and the potential of our methods, in multiple text classification contexts. This dissertation is, to the best of our knowledge, the first to systematically address the task of semi-automated text classification

Development of urban area geospatial information products from high resolution satellite imagery using advanced image analysis techniques

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (June 29, 2005)Includes bibliographical references.Vita.Thesis (Ph. D.) University of Missouri-Columbia 2004.Dissertations, Academic -- University of Missouri--Columbia -- Electrical engineering.The latest generation of commercial satellite imaging sensors have a number of characteristics (e.g. high spatial resolution, multispectral bands, and quick revisit time), that make them ideal data sources for a variety of urban area applications. The goal of this doctoral research was to develop advanced automated and semi-automated image analysis and classification techniques for the extraction of urban area geospatial information products from commercial high-resolution satellite imagery. We developed two semi-automated urban land cover classification approaches, as well as fully automated techniques for road network and 2-D building footprint extraction. By utilizing fully automated feature extraction techniques for training data generation, a self-supervised classification approach was also developed. The self-supervised classifier is significantly more accurate than traditional classification approaches, and unlike traditional approaches it is fully automated. The development of automated and semi-automated techniques for generation of urban geospatial information products is of high importance not only for the many applications where they can be used but also because the large volume of data collected by these sensors exceeds the human capacity of trained image specialists to analyze. In addition, many applications, especially those for the military and intelligence communities, require near real time exploitation of the image data

Nomenclature and Benchmarking Models of Text Classification Models: Contemporary Affirmation of the Recent Literature

In this paper we present automated text classification in text mining that is gaining greater relevance in various fields every day Text mining primarily focuses on developing text classification systems able to automatically classify huge volume of documents comprising of unstructured and semi structured data The process of retrieval classification and summarization simplifies extract of information by the user The finding of the ideal text classifier feature generator and distinct dominant technique of feature selection leading all other previous research has received attention from researchers of diverse areas as information retrieval machine learning and the theory of algorithms To automatically classify and discover patterns from the different types of the documents 1 techniques like Machine Learning Natural Language Processing NLP and Data Mining are applied together In this paper we review some effective feature selection researches and show the results in a table for

Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty

Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real applications. A priori approximations of the difficulty to link a particular entity mention can facilitate flagging of critical cases as part of semi-automated EL systems, while detecting latent factors that affect the EL performance, like corpus-specific features, can provide insights on how to improve a system based on the special characteristics of the underlying corpus. In this paper, we first introduce a consensus-based method to generate difficulty labels for entity mentions on arbitrary corpora. The difficulty labels are then exploited as training data for a supervised classification task able to predict the EL difficulty of entity mentions using a variety of features. Experiments over a corpus of news articles show that EL difficulty can be estimated with high accuracy, revealing also latent features that affect EL performance. Finally, evaluation results demonstrate the effectiveness of the proposed method to inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP Symposium On Applied Computing (SAC 2019

Literature classification for semi-automated updating of biological knowledgebases

BACKGROUND: As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. RESULTS: We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. CONCLUSION: We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases

On the use of text classification methods for text summarisation

This thesis describes research work undertaken in the fields of text and questionnaire mining. More specifically, the research work is directed at the use of text classification techniques for the purpose of summarising the free text part of questionnaires. In this thesis text summarisation is conceived of as a form of text classification in that the classes assigned to text documents can be viewed as an indication (summarisation) of the main ideas of the original free text but in a coherent and reduced form. The reason for considering this type of summary is because summarising unstructured free text, such as that found in questionnaires, is not deemed to be effective using conventional text summarisation techniques. Four approaches are described in the context of the classification summarisation of free text from different sources, focused on the free text part of questionnaires. The first approach considers the use of standard classification techniques for text summarisation and was motivated by the desire to establish a benchmark with which the more specialised summarisation classification techniques presented later in this thesis could be compared. The second approach, called Classifier Generation Using Secondary Data (CGUSD), addresses the case when the available data is not considered sufficient for training purposes (or possibly because no data is available at all). The third approach, called Semi-Automated Rule Summarisation Extraction Tool (SARSET), presents a semi-automated classification technique to support document summarisation classification in which there is more involvement by the domain experts in the classifier generation process, the idea was that this might serve to produce more effective summaries. The fourth is a hierarchical summarisation classification approach which assumes that text summarisation can be achieved using a classification approach whereby several class labels can be associated with documents which then constitute the summarisation. For evaluation purposes three types of text were considered: (i) questionnaire free text, (ii) text from medical abstracts and (iii) text from news stories