By Manuela Angioni, Emanuela De Vita (auth.), Vincenzo Pallotta, Alessandro Soro, Eloisa Vargiu (eds.)
This quantity comprises revised and prolonged models of papers awarded on the 4th version of the overseas workshop on disbursed and Agent-based Retrieval Toos (DART'10) held in June 2010, at the side of the Symposium on Human Language expertise for the data Society, in Geneva, Swizerland. Practitioners and researchers engaged on pervasive and clever entry to net providers and disbursed details retrieval met to proportion their effects and insights in fascinating and demanding themes comparable to: (i) social media and collaboration, (ii) new demanding situations in seek expertise, (iii) sentiment research and opinion mining, (iv) disbursed info retrieval, (v) pervasive intelligence. each bankruptcy, ahead of discussing extensive the explicit subject, offers a finished overview of similar paintings and state-of-the-art, 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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Additional resources for Advances in Distributed Agent-Based Retrieval Tools
To select the first mentions of a reference chain or a possible antecedent, CalcRef uses the global weight of the candidates. The global weight is the sum of the global accessibility weight and the syntactic role weight. Thus, each expression weight is augmented by a syntactic weight. The syntactic weight is 100 for the subject, 50 for the direct object, 30 for the indirect object and 20 for other syntactic functions. Then, we use genre-dependent parameters (such as the preference for the first element type or the distance between the mentions) to increase the weight (+50) of some candidates.
For each pair, we check some strong and weak constraints, defined for French. Weak constraints mean that they might not be satisfied, even if there is a valid antecedent-anaphor pair: agreement in gender or number, similar syntactic function, semantic knowledge (person names might be valid antecedents of a noun phrase expressing a function). For example, we might have a different syntactic function between antecedent (“le rapport annuel concernant l’emploi de la langue française”/’annual report on the use of French’) and anaphor (“ce rapport”/’this report’), and the two elements are related by a coreference relation: “ La Commission a présenté au Parlement le rapport annuel concernant l’emploi de la langue française.
Few resources are available for French: some corpora annotated for anaphoric relations, or definite descriptions (Manuélian 2003), but there is no large reference corpus annotated with reference chains in French1 (Salmon-Alt 2001) to apply machine learning techniques. Because no French reference corpus is available, we propose a new knowledge poor method for coreference identification. We adopt robust methods such as those used for pronoun (Mitkov 2001) and for coreference resolution (Hartrumpf 2001), (Popescu-Belis 1999), (Bontcheva et al.