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2012
Conference article
Open Access
Blog distillation via sentiment-sensitive link analysis.
Berardi G., Esuli A., Sebastiani F., Silvestri F.
In this paper we approach blog distillation by adding a link analysis phase to the standard retrieval-by-topicality phase, where we also we check whether a given hyperlink is a citation with a positive or a negative nature. This allows us to test the hypothesis that distinguishing approval from disapproval brings about benefits in blog distillation.Source: Natural Language Processing and Information Systems. 17th International Conference on Applications of Natural Language to Information Systems, pp. 228–233, Groningen, The Netherlands, 26-28 June 2012
DOI: 10.1007/978-3-642-31178-9_26
Metrics:
Berardi G., Esuli A., Sebastiani F., Silvestri F.
In this paper we approach blog distillation by adding a link analysis phase to the standard retrieval-by-topicality phase, where we also we check whether a given hyperlink is a citation with a positive or a negative nature. This allows us to test the hypothesis that distinguishing approval from disapproval brings about benefits in blog distillation.Source: Natural Language Processing and Information Systems. 17th International Conference on Applications of Natural Language to Information Systems, pp. 228–233, Groningen, The Netherlands, 26-28 June 2012
DOI: 10.1007/978-3-642-31178-9_26
Metrics:
See at:
nmis.isti.cnr.it 
| doi.org 
| www.scopus.com | www.springerlink.com | CNR ExploRA
2012
Conference article
Open Access
ISTI@ TREC Microblog track 2012: real-time filtering through supervised learning
Berardi G., Esuli A., Marcheggiani D.
Our approach to the microblog filtering task is based on learning a relevance classifier from an initial training set of relevant and non relevant tweets, generated by using a simple retrieval method. The classifier is then retrained using the (simulated) user feedback collected during the training process, in order to improve its accuracy as the filtering process goes on. In the official runs the system scored low effectiveness values, suffering a strong imbalance toward recall.Source: TRC 2012 - 21th Text Retrieval Conference, Gaithersburg, US, 6-9 November 2012
Berardi G., Esuli A., Marcheggiani D.
Our approach to the microblog filtering task is based on learning a relevance classifier from an initial training set of relevant and non relevant tweets, generated by using a simple retrieval method. The classifier is then retrained using the (simulated) user feedback collected during the training process, in order to improve its accuracy as the filtering process goes on. In the official runs the system scored low effectiveness values, suffering a strong imbalance toward recall.Source: TRC 2012 - 21th Text Retrieval Conference, Gaithersburg, US, 6-9 November 2012
See at:
trec.nist.gov | CNR ExploRA
2012
Conference article
Open Access
Metadata enrichment services for the Europeana digital library.
Berardi G., Esuli A., Gordea S., Marcheggiani D., Sebastiani F.
We demonstrate a metadata enrichment system for the Europeana digital library. The system allows different institutions which provide to Europeana pointers (in the form of metadata records - MRs) to their content to enrich their MRs by classifying them under a classification scheme of their choice, and to extract/highlight entities of significant interest within the MRs themselves. The use of a supervised learning metaphor allows each content provider (CP) to generate classifiers and extractors tailored to the CP's specific needs, thus allowing the tool to be effectively available to the multitude (2000+) of Europeana CPs.Source: Theory and Practice of Digital Libraries. Second International Conference, pp. 508–511, Paphos, Cyprus, 23-27 September 2012
DOI: 10.1007/978-3-642-33290-6_61
Metrics:
Berardi G., Esuli A., Gordea S., Marcheggiani D., Sebastiani F.
We demonstrate a metadata enrichment system for the Europeana digital library. The system allows different institutions which provide to Europeana pointers (in the form of metadata records - MRs) to their content to enrich their MRs by classifying them under a classification scheme of their choice, and to extract/highlight entities of significant interest within the MRs themselves. The use of a supervised learning metaphor allows each content provider (CP) to generate classifiers and extractors tailored to the CP's specific needs, thus allowing the tool to be effectively available to the multitude (2000+) of Europeana CPs.Source: Theory and Practice of Digital Libraries. Second International Conference, pp. 508–511, Paphos, Cyprus, 23-27 September 2012
DOI: 10.1007/978-3-642-33290-6_61
Metrics:
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nmis.isti.cnr.it | doi.org | link.springer.com | CNR ExploRA
2012
Conference article
Open Access
A utility-theoretic ranking method for semi-automated text classification.
Berardi G., Esuli A., Sebastiani F.
In Semi-Automated Text Classification (SATC) an automatic classifier Phi labels a set of unlabelled documents D, following which a human annotator inspects (and corrects when appropriate) the labels attributed by Phi to a subset D' of D, with the aim of improving the overall quality of the labelling. An automated system can support this process by ranking the automatically labelled documents in a way that maximizes the expected increase in effectiveness that derives from inspecting D'. An obvious strategy is to rank D so that the documents that Phi has classified with the lowest confidence are top-ranked. In this work we show that this strategy is suboptimal. We develop a new utility-theoretic ranking method based on the notion of inspection gain, defined as the improvement in classification effectiveness that would derive by inspecting and correcting a given automatically labelled document. We also propose a new effectiveness measure for SATC-oriented ranking methods, based on the expected reduction in classification error brought about by partially inspecting a 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 measure, our ranking method can achieve substantially higher expected reductions in classification error.Source: The 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 961–970, Portland, Oregon, USA, 12-16 August 2012
DOI: 10.1145/2348283.2348411
Metrics:
Berardi G., Esuli A., Sebastiani F.
In Semi-Automated Text Classification (SATC) an automatic classifier Phi labels a set of unlabelled documents D, following which a human annotator inspects (and corrects when appropriate) the labels attributed by Phi to a subset D' of D, with the aim of improving the overall quality of the labelling. An automated system can support this process by ranking the automatically labelled documents in a way that maximizes the expected increase in effectiveness that derives from inspecting D'. An obvious strategy is to rank D so that the documents that Phi has classified with the lowest confidence are top-ranked. In this work we show that this strategy is suboptimal. We develop a new utility-theoretic ranking method based on the notion of inspection gain, defined as the improvement in classification effectiveness that would derive by inspecting and correcting a given automatically labelled document. We also propose a new effectiveness measure for SATC-oriented ranking methods, based on the expected reduction in classification error brought about by partially inspecting a 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 measure, our ranking method can achieve substantially higher expected reductions in classification error.Source: The 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 961–970, Portland, Oregon, USA, 12-16 August 2012
DOI: 10.1145/2348283.2348411
Metrics:
See at:
nmis.isti.cnr.it | doi.org | CNR ExploRA