Search (151 results, page 3 of 8)

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Source

Classification in the 1970s: a second look. Rev. ed. Ed.: A. Maltby

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Abstract

Recent studies of authorship attribution have used machine-learning methods including regularized multinomial logistic regression, neural nets, support vector machines, and the nearest shrunken centroid classifier to identify likely authors of disputed texts. These methods are all limited by an inability to perform open-set classification and account for text and corpus size. We propose a customized Bayesian logit-normal-beta-binomial classification model for supervised authorship attribution. The model is based on the beta-binomial distribution with an explicit inverse relationship between extra-binomial variation and text size. The model internally estimates the relationship of extra-binomial variation to text size, and uses Markov Chain Monte Carlo (MCMC) to produce distributions of posterior authorship probabilities instead of point estimates. We illustrate the method by training the machine-learning methods as well as the open-set Bayesian classifier on undisputed papers of The Federalist, and testing the method on documents historically attributed to Alexander Hamilton, John Jay, and James Madison. The Bayesian classifier was the best classifier of these texts.

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Abstract

The Wolverhampton Web Library (WWLib) is a WWW search engine that provides access to UK based information. The experimental version developed in 1995, was a success but highlighted the need for a much higher degree of automation. An interesting feature of the experimental WWLib was that it organised information according to DDC. Discusses the advantages of classification and describes the automatic classifier that is being developed in Java as part of the new, fully automated WWLib

Date

1. 8.1996 22:08:06

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Abstract

The classification of documents from a bibliographic database is a task that is linked to processes of information retrieval based on partial matching. A method is described of vectorizing reference documents from LISA which permits their topological organization using Kohonen's algorithm. As an example a map is generated of 202 documents from LISA, and an analysis is made of the possibilities of this type of neural network with respect to the development of information retrieval systems based on graphical browsing.

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Abstract

In the Thomson Reuters Web of Science database, the subject categories of a journal are applied to all articles in the journal. However, many articles in multidisciplinary Sciences journals may only be represented by a small number of subject categories. To provide more accurate information on the research areas of articles in such journals, we can classify articles in these journals into subject categories as defined by Web of Science based on their references. For an article in a multidisciplinary sciences journal, the method counts the subject categories in all of the article's references indexed by Web of Science, and uses the most numerous subject categories of the references to determine the most appropriate classification of the article. We used articles in an issue of Proceedings of the National Academy of Sciences (PNAS) to validate the correctness of the method by comparing the obtained results with the categories of the articles as defined by PNAS and their content. This study shows that the method provides more precise search results for the subject category of interest in bibliometric investigations through recognition of articles in multidisciplinary sciences journals whose work relates to a particular subject category.

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Abstract

While automated methods for information organization have been around for several decades now, exponential growth of the World Wide Web has put them into the forefront of research in different communities, within which several approaches can be identified: 1) machine learning (algorithms that allow computers to improve their performance based on learning from pre-existing data); 2) document clustering (algorithms for unsupervised document organization and automated topic extraction); and 3) string matching (algorithms that match given strings within larger text). Here the aim was to automatically organize textual documents into hierarchical structures for subject browsing. The string-matching approach was tested using a controlled vocabulary (containing pre-selected and pre-defined authorized terms, each corresponding to only one concept). The results imply that an appropriate controlled vocabulary, with a sufficient number of entry terms designating classes, could in itself be a solution for automated classification. Then, if the same controlled vocabulary had an appropriat hierarchical structure, it would at the same time provide a good browsing structure for the collection of automatically classified documents.

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Abstract

A very important extension of the traditional domain of knowledge organization (KO) arises from attempts to incorporate techniques devised in the computer science domain for automatic concept extraction and for grouping, categorizing, clustering and otherwise organizing knowledge using mechanical means. Four specific terms have emerged to identify the most prevalent techniques: machine learning, clustering, automatic indexing, and automatic classification. Our study presents three domain analytical case analyses in search of answers. The first case relies on citations located using the ISKO-supported "Knowledge Organization Bibliography." The second case relies on works in both Web of Science and SCOPUS. Case three applies co-word analysis and citation analysis to the contents of the papers in the present special issue. We observe scholars involved in "clustering" and "automatic classification" who share common thematic emphases. But we have found no coherence, no common activity and no social semantics. We have not found a research front, or a common teleology within the KO domain. We also have found a lively group of authors who have succeeded in submitting papers to this special issue, and their work quite interestingly aligns with the case studies we report. There is an emphasis on KO for information retrieval; there is much work on clustering (which involves conceptual points within texts) and automatic classification (which involves semantic groupings at the meta-document level).

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Abstract

Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.

Content

Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.

Series

Proceedings of the ... annual conference of the Gesellschaft für Klassifikation e.V. ; 24)(Studies in classification, data analysis, and knowledge organization

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Abstract

Students use general web search engines as their primary source of research while trying to find answers to school-related questions. Although search engines are highly relevant for the general population, they may return results that are out of educational context. Another rising trend; social community question answering websites are the second choice for students who try to get answers from other peers online. We attempt discovering possible improvements in educational search by leveraging both of these information sources. For this purpose, we first implement a classifier for educational questions. This classifier is built by an ensemble method that employs several regular learning algorithms and retrieval based approaches that utilize external resources. We also build a query expander to facilitate classification. We further improve the classification using search engine results and obtain 83.5% accuracy. Although our work is entirely based on the Turkish language, the features could easily be mapped to other languages as well. In order to find out whether search engine ranking can be improved in the education domain using the classification model, we collect and label a set of query results retrieved from a general web search engine. We propose five ad-hoc methods to improve search ranking based on the idea that the query-document category relation is an indicator of relevance. We evaluate these methods for overall performance, varying query length and based on factoid and non-factoid queries. We show that some of the methods significantly improve the rankings in the education domain.

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Abstract

With the increase of information on the Web, it is difficult to find desired information quickly out of the documents retrieved by a search engine. One way to solve this problem is to classify web documents according to various criteria. Most document classification has been focused on a subject or a topic of a document. A genre or a style is another view of a document different from a subject or a topic. The genre is also a criterion to classify documents. In this paper, we suggest multiple sets of features to classify genres of web documents. The basic set of features, which have been proposed in the previous studies, is acquired from the textual properties of documents, such as the number of sentences, the number of a certain word, etc. However, web documents are different from textual documents in that they contain URL and HTML tags within the pages. We introduce new sets of features specific to web documents, which are extracted from URL and HTML tags. The present work is an attempt to evaluate the performance of the proposed sets of features, and to discuss their characteristics. Finally, we conclude which is an appropriate set of features in automatic genre classification of web documents.

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Date

1. 2.2016 18:25:22

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Abstract

The goal of this research is to clarify the role of document classification in information filtering. An important function of classification, in managing computational complexity, is described and illustrated in the context of an existing filtering system. A parameter called classification homogeneity is presented for analyzing unsupervised automated classification by employing human classification as a control. 2 significant components of the automated classification approach, vocabulary discovery and classification scheme generation, are described in detail. Results of classification performance revealed considerable variability in the homogeneity of automatically produced classes. Based on the classification performance, different types of interest profiles were created. Subsequently, these profiles were used to perform filtering sessions. The filtering results showed that with increasing homogeneity, filtering performance improves, and, conversely, with decreasing homogeneity, filtering performance degrades

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Abstract

Millions of micro texts are published every day on Twitter. Identifying the sentiment present in them can be helpful for measuring the frame of mind of the public, their satisfaction with respect to a product, or their support of a social event. In this context, polarity classification is a subfield of sentiment analysis focused on determining whether the content of a text is objective or subjective, and in the latter case, if it conveys a positive or a negative opinion. Most polarity detection techniques tend to take into account individual terms in the text and even some degree of linguistic knowledge, but they do not usually consider syntactic relations between words. This article explores how relating lexical, syntactic, and psychometric information can be helpful to perform polarity classification on Spanish tweets. We provide an evaluation for both shallow and deep linguistic perspectives. Empirical results show an improved performance of syntactic approaches over pure lexical models when using large training sets to create a classifier, but this tendency is reversed when small training collections are used.

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Abstract

In a scientific and technological watch (STW) task, an expert user needs to survey the evolution of research topics in his area of specialisation in order to detect interesting changes. The majority of methods proposing evaluation metrics (bibliometrics and scientometrics studies) for STW rely solely an statistical data analysis methods (Co-citation analysis, co-word analysis). Such methods usually work an structured databases where the units of analysis (words, keywords) are already attributed to documents by human indexers. The advent of huge amounts of unstructured textual data has rendered necessary the integration of natural language processing (NLP) techniques to first extract meaningful units from texts. We propose a method for STW which is NLP-oriented. The method not only analyses texts linguistically in order to extract terms from them, but also uses linguistic relations (syntactic variations) as the basis for clustering. Terms and variation relations are formalised as weighted di-graphs which the clustering algorithm, CPCL (Classification by Preferential Clustered Link) will seek to reduce in order to produces classes. These classes ideally represent the research topics present in the corpus. The results of the classification are subjected to validation by an expert in STW.

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Abstract

Diese Arbeit befasst sich mit den praktischen Aspekten des Automatischen Klassifizierens bibliographischer Referenzdaten. Im Vordergrund steht die konkrete Vorgehensweise anhand des eigens zu diesem Zweck entwickelten Open Source-Programms COBRA "Classification Of Bibliographic Records, Automatic". Es werden die Rahmenbedingungen und Parameter f¨ur einen Einsatz im bibliothekarischen Umfeld geklärt. Schließlich erfolgt eine Auswertung von Klassifizierungsergebnissen am Beispiel sozialwissenschaftlicher Daten aus der Datenbank SOLIS.

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Abstract

In this paper, we introduce ACS, an automatic classification system for school libraries. First, various approaches towards automatic classification, namely (i) rule-based, (ii) browse and search, and (iii) partial match, are critically reviewed. The central issues of scheme selection, text analysis and similarity measures are discussed. A novel approach towards detecting book-class similarity with Modified Overlap Coefficient (MOC) is also proposed. Finally, the design and implementation of ACS is presented. The test result of over 80% correctness in automatic classification and a cost reduction of 75% compared to manual classification suggest that ACS is highly adoptable

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Abstract

An automatic classification technique has been developed, based on the character structure of words. Dice's similarity coefficient is computed from the number of matching diagrams in pairs of character strings, and used to cluster sets of character strings. A sample of words from a chemical data base was chosen to contain certain stems derived from the names of chemical elements. They were successfully clusterd into groups of semantically related words. Each cluster is characterised by the root word from which all its members are derived. A second example of titles from Mathematical Reviews was clustered into well-defined classes, which compare favourably with the subject groupings of Mathematical Reviews

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Abstract

This working paper describes the creation of a test database to carry out the automatic classification tasks of the DESIRE II work package D3.6a on. It is an improved version of NetLab's existing "All" Engineering database created after a comparative study of the outcome of two different approaches to collecting the documents. These two methods were selected from seven different general methodologies to build robot-generated subject indices, presented in this paper. We found a surprisingly low overlap between the Engineering link collections we used as seed pages for the robot and subsequently an even more surprisingly low overlap between the resources collected by the two different approaches. That inspite of using basically the same services to start the harvesting process from. A intellectual evaluation of the contents of both databases showed almost exactly the same percentage of relevant documents (77%), indicating that the main difference between those aproaches was the coverage of the resulting database.

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Abstract

This paper addresses the problem of automatically assigning a Library of Congress Classification (LCC) to a work given its set of Library of Congress Subject Headings (LCSH). LCCs are organized in a tree: The root node of this hierarchy comprises all possible topics, and leaf nodes correspond to the most specialized topic areas defined. We describe a procedure that, given a resource identified by its LCSH, automatically places that resource in the LCC hierarchy. The procedure uses machine learning techniques and training data from a large library catalog to learn a model that maps from sets of LCSH to classifications from the LCC tree. We present empirical results for our technique showing its accuracy an an independent collection of 50,000 LCSH/LCC pairs.

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Abstract

In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. However, associative classifiers also have some obstacles to overcome when they are applied in the area of text classification. The target text collection generally has a very high dimension, thus the training process might take a very long time. We propose a feature selection based on the mutual information between the word and class variables to reduce the space dimension of the associative classifiers. In addition, the training process of the associative classifier produces a huge amount of classification rules, which makes the prediction with a new document ineffective. We resolve this by introducing a new efficient method for storing and pruning classification rules. This method can also be used when predicting a test document. Experimental results using the 20-newsgroups dataset show many benefits of the associative classification in both training and predicting when applied to a real world problem.