Advances in Distributed Agent-Based Retrieval Tools by Manuela Angioni, Emanuela De Vita (auth.), Vincenzo

By Manuela Angioni, Emanuela De Vita (auth.), Vincenzo Pallotta, Alessandro Soro, Eloisa Vargiu (eds.)

This quantity includes revised and prolonged types of papers provided on the 4th version of the foreign workshop on dispensed and Agent-based Retrieval Toos (DART'10) held in June 2010, along with the Symposium on Human Language expertise for the knowledge Society, in Geneva, Swizerland. Practitioners and researchers engaged on pervasive and clever entry to net companies and allotted details retrieval met to percentage their effects and insights in fascinating and difficult issues resembling: (i) social media and collaboration, (ii) new demanding situations in seek expertise, (iii) sentiment research and opinion mining, (iv) dispensed info retrieval, (v) pervasive intelligence. each bankruptcy, ahead of discussing extensive the categorical subject, offers a accomplished assessment of comparable paintings and cutting-edge, within the desire of this quantity to be of use within the years yet to come, to either researchers and students.

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Longo and A. Todiraşcu Linguistic markers Text Statistical segmentation C99 (Choi 2001) and / or Syntactic markers List of topics Topic candidates and / or Heuristic rules Discourse cues Reference chains Fig. 1 The architecture of the topic detection system 3 The Reference Chains (Schnedecker 1997) considers reference chains as cohesion markers. A reference chain contains at least three mentions (three referential expressions) referring to the same unique entity. In the example below, we have three mentions for the referent ‘Mr Pons’: “M.

W b . j ille urg n err ec ht en Fig. 2 A word alignment matrix In Figure 2 we see the intersection of links illustrated by filled boxes. Additional alignment points from the union of links are shown as empty boxes. Translational contexts are used to find distributionally similar words. We give some examples of translational co-occurrence vectors in Table 1. Every cell in the vector refers to a particular translational co-occurrence type. For example, chat ‘cat’ gets the translation Katze in German.

In addition, we apply several annotation modules to automatically identify the referential expressions (named entities, complex noun phrases) that may be potential candidates as the first mention of a reference chain. Also, we automatically annotate the impersonal occurrences of the French pronoun “il” to avoid wrong anaphora candidates (the system will ignore these impersonal non-anaphoric occurrences). Using these automatic annotations, CalcRef computes the reference chains. It first selects the potential candidates for the first mention of a chain (according to Annotations Tagged text TTL (Ion, 2007) Annotation of Complex Noun Phrases Fig.

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