-
Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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- Abstract
- Document representations for text classification are typically based on the classical Bag-Of-Words paradigm. This approach comes with deficiencies that motivate the integration of features on a higher semantic level than single words. In this paper we propose an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting is used for actual classification. Experimental evaluations on two well known text corpora support our approach through consistent improvement of the results.
- Content
- Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
- Date
- 8. 1.2013 10:22:32
-
Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007)
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- Abstract
- The proliferation of digital resources and their integration into a traditional library setting has created a pressing need for an automated tool that organizes textual information based on library classification schemes. Automated text classification is a research field of developing tools, methods, and models to automate text classification. This article describes the current popular approach for text classification and major text classification projects and applications that are based on library classification schemes. Related issues and challenges are discussed, and a number of considerations for the challenges are examined.
- Date
- 22. 9.2008 18:31:54
- Source
- International cataloguing and bibliographic control. 36(2007) no.4, S.78-82
-
Golub, K.; Hansson, J.; Soergel, D.; Tudhope, D.: Managing classification in libraries : a methodological outline for evaluating automatic subject indexing and classification in Swedish library catalogues (2015)
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- Abstract
- Subject terms play a crucial role in resource discovery but require substantial effort to produce. Automatic subject classification and indexing address problems of scale and sustainability and can be used to enrich existing bibliographic records, establish more connections across and between resources and enhance consistency of bibliographic data. The paper aims to put forward a complex methodological framework to evaluate automatic classification tools of Swedish textual documents based on the Dewey Decimal Classification (DDC) recently introduced to Swedish libraries. Three major complementary approaches are suggested: a quality-built gold standard, retrieval effects, domain analysis. The gold standard is built based on input from at least two catalogue librarians, end-users expert in the subject, end users inexperienced in the subject and automated tools. Retrieval effects are studied through a combination of assigned and free tasks, including factual and comprehensive types. The study also takes into consideration the different role and character of subject terms in various knowledge domains, such as scientific disciplines. As a theoretical framework, domain analysis is used and applied in relation to the implementation of DDC in Swedish libraries and chosen domains of knowledge within the DDC itself.
- Source
- Classification and authority control: expanding resource discovery: proceedings of the International UDC Seminar 2015, 29-30 October 2015, Lisbon, Portugal. Eds.: Slavic, A. u. M.I. Cordeiro
-
Wang, J.: ¬An extensive study on automated Dewey Decimal Classification (2009)
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- Abstract
- In this paper, we present a theoretical analysis and extensive experiments on the automated assignment of Dewey Decimal Classification (DDC) classes to bibliographic data with a supervised machine-learning approach. Library classification systems, such as the DDC, impose great obstacles on state-of-art text categorization (TC) technologies, including deep hierarchy, data sparseness, and skewed distribution. We first analyze statistically the document and category distributions over the DDC, and discuss the obstacles imposed by bibliographic corpora and library classification schemes on TC technology. To overcome these obstacles, we propose an innovative algorithm to reshape the DDC structure into a balanced virtual tree by balancing the category distribution and flattening the hierarchy. To improve the classification effectiveness to a level acceptable to real-world applications, we propose an interactive classification model that is able to predict a class of any depth within a limited number of user interactions. The experiments are conducted on a large bibliographic collection created by the Library of Congress within the science and technology domains over 10 years. With no more than three interactions, a classification accuracy of nearly 90% is achieved, thus providing a practical solution to the automatic bibliographic classification problem.
-
Golub, K.; Lykke, M.: Automated classification of web pages in hierarchical browsing (2009)
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- Abstract
- Purpose - The purpose of this study is twofold: to investigate whether it is meaningful to use the Engineering Index (Ei) classification scheme for browsing, and then, if proven useful, to investigate the performance of an automated classification algorithm based on the Ei classification scheme. Design/methodology/approach - A user study was conducted in which users solved four controlled searching tasks. The users browsed the Ei classification scheme in order to examine the suitability of the classification systems for browsing. The classification algorithm was evaluated by the users who judged the correctness of the automatically assigned classes. Findings - The study showed that the Ei classification scheme is suited for browsing. Automatically assigned classes were on average partly correct, with some classes working better than others. Success of browsing showed to be correlated and dependent on classification correctness. Research limitations/implications - Further research should address problems of disparate evaluations of one and the same web page. Additional reasons behind browsing failures in the Ei classification scheme also need further investigation. Practical implications - Improvements for browsing were identified: describing class captions and/or listing their subclasses from start; allowing for searching for words from class captions with synonym search (easily provided for Ei since the classes are mapped to thesauri terms); when searching for class captions, returning the hierarchical tree expanded around the class in which caption the search term is found. The need for improvements of classification schemes was also indicated. Originality/value - A user-based evaluation of automated subject classification in the context of browsing has not been conducted before; hence the study also presents new findings concerning methodology.
- Object
- Engineering Index Classification
-
Desale, S.K.; Kumbhar, R.: Research on automatic classification of documents in library environment : a literature review (2013)
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- Abstract
- This paper aims to provide an overview of automatic classification research, which focuses on issues related to the automatic classification of documents in a library environment. The review covers literature published in mainstream library and information science studies. The review was done on literature published in both academic and professional LIS journals and other documents. This review reveals that basically three types of research are being done on automatic classification: 1) hierarchical classification using different library classification schemes, 2) text categorization and document categorization using different type of classifiers with or without using training documents, and 3) automatic bibliographic classification. Predominantly this research is directed towards solving problems of organization of digital documents in an online environment. However, very little research is devoted towards solving the problems of arrangement of physical documents.
-
Reiner, U.: DDC-based search in the data of the German National Bibliography (2008)
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- Abstract
- In 2004, the German National Library began to classify title records of the German National Bibliography according to subject groups based on the divisions of the Dewey Decimal Classification (DDC). Since 2006, all titles of the main series of the German National Bibliography are classified in strict compliance with the DDC. On this basis, an enhanced DDC-based search can be realized - e.g., searching the data of the German National Bibliography for title records using number components of synthesized classification numbers or searching for DDC numbers using unclassified title records. This paper gives an account of the current research and development of the DDC-based search. The work is conducted in the VZG project Colibri that focuses on the automatic analysis of DDC-synthesized numbers and the automatic classification of bibliographic title records.
- Source
- New pespectives on subject indexing and classification: essays in honour of Magda Heiner-Freiling. Red.: K. Knull-Schlomann, u.a
-
Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012)
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- Abstract
- We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.
-
Yi, K.: Challenges in automated classification using library classification schemes (2006)
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- Abstract
- A major library classification scheme has long been standard classification framework for information sources in traditional library environment, and text classification (TC) becomes a popular and attractive tool of organizing digital information. This paper gives an overview of previous projects and studies on TC using major library classification schemes, and summarizes a discussion of TC research challenges.
-
Koch, T.; Ardö, A.: Automatic classification of full-text HTML-documents from one specific subject area : DESIRE II D3.6a, Working Paper 2 (2000)
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- Content
- 1 Introduction / 2 Method overview / 3 Ei thesaurus preprocessing / 4 Automatic classification process: 4.1 Matching -- 4.2 Weighting -- 4.3 Preparation for display / 5 Results of the classification process / 6 Evaluations / 7 Software / 8 Other applications / 9 Experiments with universal classification systems / References / Appendix A: Ei classification service: Software / Appendix B: Use of the classification software as subject filter in a WWW harvester.
-
Golub, K.; Hamon, T.; Ardö, A.: Automated classification of textual documents based on a controlled vocabulary in engineering (2007)
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- Abstract
- Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents - instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and en- richment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms.
-
Borko, H.: Research in computer based classification systems (1985)
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- Abstract
- The selection in this reader by R. M. Needham and K. Sparck Jones reports an early approach to automatic classification that was taken in England. The following selection reviews various approaches that were being pursued in the United States at about the same time. It then discusses a particular approach initiated in the early 1960s by Harold Borko, at that time Head of the Language Processing and Retrieval Research Staff at the System Development Corporation, Santa Monica, California and, since 1966, a member of the faculty at the Graduate School of Library and Information Science, University of California, Los Angeles. As was described earlier, there are two steps in automatic classification, the first being to identify pairs of terms that are similar by virtue of co-occurring as index terms in the same documents, and the second being to form equivalence classes of intersubstitutable terms. To compute similarities, Borko and his associates used a standard correlation formula; to derive classification categories, where Needham and Sparck Jones used clumping, the Borko team used the statistical technique of factor analysis. The fact that documents can be classified automatically, and in any number of ways, is worthy of passing notice. Worthy of serious attention would be a demonstra tion that a computer-based classification system was effective in the organization and retrieval of documents. One reason for the inclusion of the following selection in the reader is that it addresses the question of evaluation. To evaluate the effectiveness of their automatically derived classification, Borko and his team asked three questions. The first was Is the classification reliable? in other words, could the categories derived from one sample of texts be used to classify other texts? Reliability was assessed by a case-study comparison of the classes derived from three different samples of abstracts. The notso-surprising conclusion reached was that automatically derived classes were reliable only to the extent that the sample from which they were derived was representative of the total document collection. The second evaluation question asked whether the classification was reasonable, in the sense of adequately describing the content of the document collection. The answer was sought by comparing the automatically derived categories with categories in a related classification system that was manually constructed. Here the conclusion was that the automatic method yielded categories that fairly accurately reflected the major area of interest in the sample collection of texts; however, since there were only eleven such categories and they were quite broad, they could not be regarded as suitable for use in a university or any large general library. The third evaluation question asked whether automatic classification was accurate, in the sense of producing results similar to those obtainabie by human cIassifiers. When using human classification as a criterion, automatic classification was found to be 50 percent accurate.
- Footnote
- Original in: Classification research: Proceedings of the Second International Study Conference held at Hotel Prins Hamlet, Elsinore, Denmark, 14th-18th Sept. 1964. Ed.: Pauline Atherton. Copenhagen: Munksgaard 1965. S.220-238.
- Source
- Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al
-
Automatic classification research at OCLC (2002)
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- Abstract
- OCLC enlists the cooperation of the world's libraries to make the written record of humankind's cultural heritage more accessible through electronic media. Part of this goal can be accomplished through the application of the principles of knowledge organization. We believe that cultural artifacts are effectively lost unless they are indexed, cataloged and classified. Accordingly, OCLC has developed products, sponsored research projects, and encouraged the participation in international standards communities whose outcome has been improved library classification schemes, cataloging productivity tools, and new proposals for the creation and maintenance of metadata. Though cataloging and classification requires expert intellectual effort, we recognize that at least some of the work must be automated if we hope to keep pace with cultural change
- Date
- 5. 5.2003 9:22:09
-
Bianchini, C.; Bargioni, S.: Automated classification using linked open data : a case study on faceted classification and Wikidata (2021)
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- Abstract
- The Wikidata gadget, CCLitBox, for the automated classification of literary authors and works by a faceted classification and using Linked Open Data (LOD) is presented. The tool reproduces the classification algorithm of class O Literature of the Colon Classification and uses data freely available in Wikidata to create Colon Classification class numbers. CCLitBox is totally free and enables any user to classify literary authors and their works; it is easily accessible to everybody; it uses LOD from Wikidata but missing data for classification can be freely added if necessary; it is readymade for any cooperative and networked project.
- Footnote
- Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
- Source
- Cataloging and classification quarterly. 59(2021) no.8, p.835-852
-
Suominen, A.; Toivanen, H.: Map of science with topic modeling : comparison of unsupervised learning and human-assigned subject classification (2016)
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- Abstract
- The delineation of coordinates is fundamental for the cartography of science, and accurate and credible classification of scientific knowledge presents a persistent challenge in this regard. We present a map of Finnish science based on unsupervised-learning classification, and discuss the advantages and disadvantages of this approach vis-à-vis those generated by human reasoning. We conclude that from theoretical and practical perspectives there exist several challenges for human reasoning-based classification frameworks of scientific knowledge, as they typically try to fit new-to-the-world knowledge into historical models of scientific knowledge, and cannot easily be deployed for new large-scale data sets. Automated classification schemes, in contrast, generate classification models only from the available text corpus, thereby identifying credibly novel bodies of knowledge. They also lend themselves to versatile large-scale data analysis, and enable a range of Big Data possibilities. However, we also argue that it is neither possible nor fruitful to declare one or another method a superior approach in terms of realism to classify scientific knowledge, and we believe that the merits of each approach are dependent on the practical objectives of analysis.
-
Schiminovich, S.: Automatic classification and retrieval of documents by means of a bibliographic pattern discovery algorithm (1971)
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-
Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013)
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- Abstract
- Retrieval of disease information is often based on several key aspects such as etiology, diagnosis, treatment, prevention, and symptoms of diseases. Automatic identification of disease aspect information is thus essential. In this article, I model the aspect identification problem as a text classification (TC) problem in which a disease aspect corresponds to a category. The disease aspect classification problem poses two challenges to classifiers: (a) a medical text often contains information about multiple aspects of a disease and hence produces noise for the classifiers and (b) text classifiers often cannot extract the textual parts (i.e., passages) about the categories of interest. I thus develop a technique, PETC (Passage Extractor for Text Classification), that extracts passages (from medical texts) for the underlying text classifiers to classify. Case studies on thousands of Chinese and English medical texts show that PETC enhances a support vector machine (SVM) classifier in classifying disease aspect information. PETC also performs better than three state-of-the-art classifier enhancement techniques, including two passage extraction techniques for text classifiers and a technique that employs term proximity information to enhance text classifiers. The contribution is of significance to evidence-based medicine, health education, and healthcare decision support. PETC can be used in those application domains in which a text to be classified may have several parts about different categories.
- Date
- 28.10.2013 19:22:57
-
Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015)
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- Abstract
- This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.
- Date
- 4. 8.2015 19:22:04
-
Godby, C.J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization : subject access issues (2003)
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- Abstract
- This paper describes a set of experiments in adapting a subset of the Library of Congress Classification for use as a database for automatic classification. A high degree of concept integrity was obtained when subject headings were mapped from OCLC's WorldCat database and filtered using the log-likelihood statistic.
- Source
- Subject retrieval in a networked environment: Proceedings of the IFLA Satellite Meeting held in Dublin, OH, 14-16 August 2001 and sponsored by the IFLA Classification and Indexing Section, the IFLA Information Technology Section and OCLC. Ed.: I.C. McIlwaine
-
Godby, C. J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization (2001)
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- Abstract
- This paper describes a set of experiments in adapting a subset of the Library of Congress Classification for use as a database for automatic classification. A high degree of concept integrity was obtained when subject headings were mapped from OCLC's WorldCat database and filtered using the log-likelihood statistic
- Footnote
- Paper, IFLA Preconference "Subject Retrieval in a Networked Environment", Dublin, OH, August 2001.
