Download Digital Libraries and Archives: 7th Italian Research by Claudio Taranto, Nicola Di Mauro, Floriana Esposito (auth.), PDF

By Claudio Taranto, Nicola Di Mauro, Floriana Esposito (auth.), Maristella Agosti, Floriana Esposito, Carlo Meghini, Nicola Orio (eds.)

This e-book constitutes the completely refereed lawsuits of the seventh Italian study convention on electronic Libraries held in Pisa, Italy, in January 2011. The 20 revised complete papers provided have been rigorously reviewed and canopy themes of curiosity equivalent to process interoperability and knowledge integration; formal and methodological foundations of electronic libraries; semantic net and associated information for electronic libraries; multilingual details entry; electronic library infrastructures; metadata production and administration; se's for electronic library platforms; overview and log information; dealing with audio/visual and non-traditional gadgets; person interfaces and visualization; electronic library quality.

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Additional info for Digital Libraries and Archives: 7th Italian Research Conference, IRCDL 2011, Pisa, Italy, January 20-21, 2011. Revised Papers

Example text

The result is a number between 0 and 1. Highest values represent the presence of a phrase at the very beginning of the document. – Phrase Last Occurrence. , in conclusion and discussion parts). The last occurrence value of a phrase is calculated as the number of words preceding the last occurrence of the phrase normalized to the total number of words in the document. The last occurrence value for phrase P in a document D is: last occurrence(P, D) = last index(P ) , size(D) where last index(P ) is the number of words preceding the phrase’s last appearance and size(D) is the total number of words in D.

By using this setting (which does not include bi-grams and tri-grams) we computed a new set of recommendations to be used as a baseline reference for performance. 2 where the precision is computed as the ratio among the number of correct recommendations and the number of produced recommendations. Table 1. The precision of the proposed approach vs. 73 The table shows that by tanking into account also bi-grams and tri-grams it is possible to obtain an higher precision. 5 Conclusion and Future Work In this work we introduced a content-based paper recommender system which produces rich user profiles and resource descriptions by extracting keyphrases from scientific articles.

Clearly, the observations sequence represents the semantic description specified by the user need. In literature, this problem is solved using the max-sum algorithm, which in HMMs applications is known as the Viterbi algorithm. The algorithm efficiently searches in the space of paths, in order to find the most probable one, with a computational cost that grows only linearly with the length of the chain. The algorithm is composed by a forward computation to find the maximization for the most probable path, and by a backward computation to decode the sequence of states.

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