Browsing by Author "Ha-Thuc, Viet"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Exploring the legal discovery and enterprise tracks at the University of Iowa
    (2007) Almquist, Brian; Ha-Thuc, Viet; Sehgal, Aditya K.; Arens, Robert; Srinivasan, Padmini
    In designing our own toolset for the TREC Legal Track, we opted to use the Lucene library of indexing and search tools. Lucene, developed in Java, is highly scalable and extendable. Indexing and searching the TREC-Legal collection proved well within Lucene’s capabilities. We indexed the entire TREC collection, opting to merge the document content and the title into a single field, using the Lucene StandardAnalyzer, which strips punctuation, but recognizes and retains elements such as e-mail addresses. The StandardAnalyzer stoplist was used for indexing. For our explorations, we converted topic fields into term vectors for querying the collection. For each topic, our system returned a ranked set of results with enough documents to match in quantity either those retrieved by a reference Boolean query executed on behalf of the TREC 2006 evaluators, or enough to reach a set cap on the number of documents returned, whichever was greater.
  • Thumbnail Image
    Item
    The University of Iowa at TREC 2008 legal and relevance feedback tracks
    (2008) Almquist, Brian; Mejova, Yelena; Ha-Thuc, Viet; Srinivasan, Padmini
    This is the second year that our research group has participated in the TREC Legal Track. Our ad hoc retrieval system has been modified to extract the additional Boolean query fields added to the 2008 topics, and to privilege documents found by the Boolean reference run when conducting our queries. We have also submitted runs that fuse the results from existing runs. For the relevance feedback task, our system uses ranking information of relevant and non-relevant documents from previously submitted runs to the TREC Legal Track to train a classifier. The classifier is applied to the remaining unjudged documents to create a new ranked list. This approach is applied to sets of input runs, including a hybrid run where a classifier trained on one set of runs is applied to the unjudged documents from another set of runs.