By Janusz S. Bień (auth.), Raffaella Bernardi, Sally Chambers, Björn Gottfried, Frédérique Segond, Ilya Zaihrayeu (eds.)
This publication constitutes the completely refereed put up complaints of 2 foreign workshops on certain facets of electronic libraries, specifically the 1st overseas Workshop on typical Language Processing for electronic Libraries, NLP4DL 2009, held in Viareggio, Italy in June 2009 and the CACAO undertaking Workshop complicated applied sciences for electronic Libraries, AT4DL 2009, held in Trento, Italy in September 2009.
A new open name was once despatched after the workshops. The revised complete papers awarded on the workshops and the newly submitted ones went via rounds of reviewing and revision. the ten papers chosen deal with a variety of elements of NLP in electronic libraries, seek, type, and electronic record processing.
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Extra info for Advanced Language Technologies for Digital Libraries: International Workshops on NLP4DL 2009, Viareggio, Italy, June 15, 2009 and AT4DL 2009, Trento, Italy, September 8, 2009
With reference to the application of machine learning techniques to the domain of digital libraries, Krowne and Halbert (2005)  presented a comparative evaluation of four automatically generated browsing interfaces based on diﬀerent classiﬁcation schemes. The authors used between 21 and 25 diﬀerent categories as their classiﬁcation schemes and represented each record by concatenating the Dublin Core ﬁelds title, description, and subject fields. Subsequently, diﬀerent classiﬁcation and clustering techniques (SVM, Na¨ıve Bayes, Rochio, and kNN ) on the article set of the Encyclopedia of Southern Culture were applied.
Nl/europeana/session/search Moving towards Adaptive Search in Digital Libraries 45 Fig. 1. Partial Domain Model Derived from TEL Log Data Using such an internal representation allows numerous potential display and interrogation techniques to be presented to the user. In this work we focus on using relations encoded in the model as query modification suggestions in guided search. Applied differently, the domain model could also be used to browse or navigate the collection. g. . We will use the analogy of ant colony optimization (ACO) to first populate and then adapt the graph.
As Table 5 and 6 depict, categories with suﬃcient training examples assigned show good results in terms of classiﬁcation accuracy. However, on the third level (see Table 5 and 6) only few categories are covered with a suﬃcent number of positive examples. More precisely, the used corpus features show an asymmetric distribution (see Figure 5 and 6) of documents across the classes (issues of data sparseness). g. g. humanistic) do not even exist. Considering only the second DDC level, we were only able to populate not even about half of the classes with enough positive examples.