68,023 research outputs found

    Information extraction from Webpages based on DOM distances

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    Retrieving information from Internet is a difficult task as it is demonstrated by the lack of real-time tools able to extract information from webpages. The main cause is that most webpages in Internet are implemented using plain (X)HTML which is a language that lacks structured semantic information. For this reason much of the efforts in this area have been directed to the development of techniques for URLs extraction. This field has produced good results implemented by modern search engines. But, contrarily, extracting information from a single webpage has produced poor results or very limited tools. In this work we define a novel technique for information extraction from single webpages or collections of interconnected webpages. This technique is based on DOM distances to retrieve information. This allows the technique to work with any webpage and, thus, to retrieve information online. Our implementation and experiments demonstrate the usefulness of the technique.Castillo, C.; Valero Llinares, H.; Guadalupe Ramos, J.; Silva Galiana, JF. (2012). Information extraction from Webpages based on DOM distances. En Computational Linguistics and Intelligent Text Processing. Springer Verlag (Germany). 181-193. doi:10.1007/978-3-642-28601-8_16S181193Dalvi, B., Cohen, W.W., Callan, J.: Websets: Extracting sets of entities from the web using unsupervised information extraction. Technical report, Carnegie Mellon School of computer Science (2011)Kushmerick, N., Weld, D.S., Doorenbos, R.: Wrapper induction for information extraction. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 1997) (1997)Cohen, W.W., Hurst, M., Jensen, L.S.: A flexible learning system for wrapping tables and lists in html documents. In: Proceedings of the international World Wide Web conference (WWW 2002), pp. 232–241 (2002)Lee, P.Y., Hui, S.C., Fong, A.C.M.: Neural networks for web content filtering. IEEE Intelligent Systems 17(5), 48–57 (2002)Anti-Porn Parental Controls Software. Porn Filtering (March 2010), http://www.tueagles.com/anti-porn/Kang, B.-Y., Kim, H.-G.: Web page filtering for domain ontology with the context of concept. IEICE - Trans. Inf. Syst. E90, D859–D862 (2007)Henzinger, M.: The Past, Present and Future of Web Information Retrieval. In: Proceedings of the 23th ACM Symposium on Principles of Database Systems (2004)W3C Consortium. Resource Description Framework (RDF), www.w3.org/RDFW3C Consortium. Web Ontology Language (OWL), www.w3.org/2004/OWLMicroformats.org. The Official Microformats Site (2009), http://microformats.orgKhare, R., Çelik, T.: Microformats: a Pragmatic Path to the Semantic Web. In: Proceedings of the 15h International Conference on World Wide Web, pp. 865–866 (2006)Khare, R.: Microformats: The Next (Small) Thing on the Semantic Web? IEEE Internet Computing 10(1), 68–75 (2006)Gupta, S., et al.: Automating Content Extraction of HTML Documents. World Wide Archive 8(2), 179–224 (2005)Li, P., Liu, M., Lin, Y., Lai, Y.: Accelerating Web Content Filtering by the Early Decision Algorithm. IEICE Transactions on Information and Systems E91-D, 251–257 (2008)W3C Consortium, Document Object Model (DOM), www.w3.org/DOMBaeza-Yates, R., Castillo, C.: Crawling the Infinite Web: Five Levels Are Enough. In: Leonardi, S. (ed.) WAW 2004. LNCS, vol. 3243, pp. 156–167. Springer, Heidelberg (2004)Micarelli, A., Gasparetti, F.: Adaptative Focused Crawling. In: The Adaptative Web, pp. 231–262 (2007)Nielsen, J.: Designing Web Usability: The Practice of Simplicity. New Riders Publishing, Indianapolis (2010) ISBN 1-56205-810-XZhang, J.: Visualization for Information Retrieval. The Information Retrieval Series. Springer, Heidelberg (2007) ISBN 3-54075-1475Hearst, M.A.: TileBars: Visualization of Term Distribution Information. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, Denver, CO, pp. 59–66 (May 1995)Gottron, T.: Evaluating Content Extraction on HTML Documents. In: Proceedings of the 2nd International Conference on Internet Technologies and Applications, pp. 123–132 (2007)Apache Foundation. The Apache crawler Nutch (2010), http://nutch.apache.or

    Automatic domain ontology extraction for context-sensitive opinion mining

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    Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

    MIR task and evaluation techniques

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    Existing tasks in MIREX have traditionally focused on low-level MIR tasks working with flat (usually DSP-only) ground-truth. These evaluation techniques, however, can not evaluate the increasing number of algorithms that utilize relational data and are not currently utilizing the state of the art in evaluating ranked or ordered output. This paper summarizes the state of the art in evaluating relational ground-truth. These components are then synthesized into novel evaluation techniques that are then applied to 14 concrete music document retrieval tasks, demonstrating how these evaluation techniques can be applied in a practical context

    Interaction platform-orientated perspective in designing novel applications

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    The lack of HCI offerings in the invention of novel software applications and the bias of design knowledge towards desktop GUI make it difficult for us to design for novel scenarios and applications that leverage emerging computational technologies. These include new media platforms such as mobiles, interactive TV, tabletops and large multi-touch walls on which many of our future applications will operate. We argue that novel application design should come not from user-centred requirements engineering as in developing a conventional application, but from understanding the interaction characteristics of the new platforms. Ensuring general usability for a particular interaction platform without rigorously specifying envisaged usage contexts helps us to design an artifact that does not restrict the possible application contexts and yet is usable enough to help brainstorm its more exact place for future exploitation
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