1,720,998 research outputs found
Hierarchical Classification of OAI Metadata Using the DDC Taxonomy
No full textIn the area of digital library services, the access to subject-specific metadata of scholarly publications is of utmost interest. One of the most prevalent approaches for metadata exchange is the XML-based Open Archive Initiative (OAI) Protocol for Metadata Harvesting (OAI-PMH). However, due to its loose requirements regarding metadata content there is no strict standard for consistent subject indexing specified, which is furthermore needed in the digital library domain. This contribution addresses the problem of automatic enhancement of OAI metadata by means of the most widely used universal classification schemes in libraries—the Dewey Decimal Classification (DDC). To be more specific, we automatically classify scientific documents according to the DDC taxonomy within three levels using a machine learning-based classifier that relies solely on OAI metadata records as the document representation. The results show an asymmetric distribution of documents across the hierarchical structure of the DDC taxonomy and issues of data sparseness. However, the performance of the classifier shows promising results on all three levels of the DDC
Sentiment Analysis Reloaded: A Comparative Study On Sentiment Polarity Identification Combining Machine Learning And Subjectivity Features
No full textWaltinger U. Sentiment Analysis Reloaded: A Comparative Study On Sentiment Polarity Identification Combining Machine Learning And Subjectivity Features. In: Proceedings of the 6th International Conference on Web Information Systems and Technologies (WEBIST '10). Valencia, Spain; 2010
On social semantics in information retrieval
Full text linkWaltinger U. On social semantics in information retrieval. Bielefeld (Germany): Bielefeld University; 2010.In this thesis we analyze the performance of social semantics in textual information retrieval. By means of collaboratively constructed knowledge derived from web-based social networks, inducing both common-sense and domain-specific knowledge as constructed by a multitude of users, we will establish an improvement in performance of selected tasks within different areas of information retrieval. This work connects the concepts and the methods of social networks and the semantic web to support the analysis of a social semantic web that combines human intelligence with machine learning and natural language processing. In this context, social networks, as instances of the social web, are capable in delivering social network data and document collections on a tremendous scale, inducing thematic dynamics that cannot be achieved by traditional expert resources. The question of an automatic conversion, annotation and processing, however, is central to the debate of the benefits of the social semantic web. Which kind of technologies and methods are available, adequate and contribute to the processing of this rapidly rising flood of information and at the same time being capable of using the wealth of information in this large, but more importantly decentralized internet. The present work researches the performance of social semantic-induced categorization by means of different document models. We will shed light on the question, to which level social networks and social ontologies contribute to selected areas within the information retrieval area, such as automatically determining term and text associations, identifying topics, text and web genre categorization, and also the domain of sentiment analysis. We will show in extensive evaluations, comparing the classical apparatus of text categorization Vector Space Model, Latent Semantic Analysis and Support Vector Maschine that significant improvements can be obtained by considering the collaborative knowledge derived from the social web
Enhancing document modeling by means of open topic models Crossing the frontier of classification schemes in digital libraries by example of the DDC
Full text linkMehler A, Waltinger U. Enhancing document modeling by means of open topic models Crossing the frontier of classification schemes in digital libraries by example of the DDC. Library Hi Tech. 2009;27(4):520-539.Purpose - The purpose of this paper is to present a topic classification model using the Dewey Decimal Classification (DDC) as the target scheme. This is to be done by exploring metadata. as provided by the Open Archives Initiative (OAT) to derive document snippets as minimal document representations. The reason is to reduce the effort of document processing in digital libraries. Further, the paper seeks to perform feature selection and extension by means of social ontologies and related web-based lexical resources. This is done to provide reliable topic-related classifications while circumventing the problem of data sparseness. Finally, the paper aims to evaluate the model by means of two language-specific corpora. The paper bridges digital libraries, on the one hand, and computational linguistics, on the other. The aim is to make accessible computational linguistic methods to provide thematic classifications in digital libraries based on closed topic models such as the DDC. Design/methodology/approach - The approach takes the form of text classification, text-technology, computational linguistics, computational semantics, and social semantics. Findings - It is shown that SVM-based classifiers perform best by exploring certain selections of OAI document metadata. Research limitations/implications - The findings show that it is necessary to further develop SVM-based DDC-classifiers by using larger training sets possibly for more than two languages in order to get better F-measure values. Originality/value - Algorithmic and formal-mathematical information is provided on how to build DDC-classifiers for digital libraries
The Feature Difference Coefficient: Classification by Means of Feature Distributions
No full textWaltinger U, Mehler A. The Feature Difference Coefficient: Classification by Means of Feature Distributions. In: Proceedings of Text Mining Services (TMS), March 23-25, Leipzig, Germany. 2009.This paper presents a model of text classification using feature frequency distribution. The proposed algorithm offers not only sensitivity to linguistic but also to structure features and calculates a unified fingerprint for each category. Classification is done by finding the closest match to prelearned models using a simple distance metric. The approach will be evaluated against three different classification scenarios. Language identification, text classification based on the Reuters corpus and web genre classification
Polarity Reinforcement: Sentiment Polarity Identification By Means Of Social Semantics
No full textWaltinger U. Polarity Reinforcement: Sentiment Polarity Identification By Means Of Social Semantics. In: Proceedings of the IEEE Africon 2009. 2009
eHumanities Desktop - eine webbasierte Arbeitsumgebung für die geisteswissenschaftliche Fachinformatik
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