Advances in Distributed Agent-Based Retrieval Tools

• Corporate Author: SpringerLink (Online service)
• Other Authors: Pallotta, Vincenzo (Editor), Soro, Alessandro (Editor), Vargiu, Eloisa (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
• Series: Studies in Computational Intelligence, 361
• Subjects: Engineering.; Artificial intelligence.; Computational intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).

 This volume contains revised and extended versions of papers presented at the 4th edition of the international workshop on Distributed and Agent-based Retrieval Toos (DART'10) held in June 2010, in conjunction with the Symposium on Human Language Technology for the Information Society, in Geneva, Swizerland. Practitioners and researchers working on pervasive and intelligent access to web services and distributed information retrieval met to share their results and insights in intriguing and challenging topics such as: (i) social media and collaboration, (ii) new challenges in search technology, (iii) sentiment analysis and opinion mining, (iv) distributed information retrieval, (v) pervasive intelligence. Every chapter, before discussing in depth the specific topic, presents a comprehensive review of related work and state of the art, in the hope of this volume to be of use in the years to come, to both researchers and students. 

 Rethinking Search Engines in Social Network Vision -- A Collaborative Web Application for Supporting Researchers in the Task of Generating Protein Datasets -- RefGen: Identifying Reference Chains to Detect Topics -- Synonym acquisition across domains and languages -- Linguistically-based Reranking of Google’s Snippets with GreG -- Opinion Mining and Sentiment Analysis Need Text Understanding -- Sentiment Analysis of French Movie Reviews -- Query Building in a Distributed Semantic Indexing System -- Building Distributed and Pervasive Information Management Systems with HDS -- Sensor Mining for User Behavior Profiling in Intelligent Environments -- Motivating Serendipitous Encounters in Museum Recommendations.