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Amélie Marian Rutgers University

Amélie Marian Rutgers University. Data Corroboration. Large amount of low-quality data Erroneous, biased, misleading,… Users have to rummage through a lot of information Data corroboration can improve the quality of results Corroboration has not been used much in practice

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Amélie Marian Rutgers University

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  1. Amélie Marian Rutgers University

  2. Data Corroboration • Large amount of low-quality data • Erroneous, biased, misleading,… • Users have to rummage through a lot of information • Data corroboration can improve the quality of results • Corroboration has not been used much in practice • Needs scoring methods • Standard ranking techniques have to be modified to handle corroborative scoring Joint work with Minji Wu (Rutgers)

  3. URSA: Understanding User Reviewing Patterns • Lots of valuable free-text information on the web • Restaurant, movie, travel, etc. reviews • Forum posts on a variety of topics • Standard search engine are not very good a searching this low-structure data • Is a review positive or negative? Keyword search is inefficient for this. • Reviews focus on different aspects: e.g., food, service, ambiance for restaurants. We want to be able to search for more precise information. Joint work with Gayatree Ganu (Rutgers), Noémie Elhadad (Columbia Biomedical Informatics)

  4. Multi-Dimensional Search in the Wayfinder File System • Typical desktop search tools use • Keyword search for ranking • Possibly some additional conditions (e.g., metadata, structure) for filtering • Information Retrieval model is insufficient for personal information systems: • Users have some imprecise information about file metadata • They want to be able to allow for fuzzy metadata and structure conditions “Find a word document file created on March 21, 2007 that contains the words “proposal draft” ” Joint work with Chris Peery, Thu D. Nguyen, Wei Wang (Rutgers)

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