Part II A legal relevance ranking function
نویسندگان
چکیده
منابع مشابه
Retrieval of Legal Documents: Combining Structured and Unstructured Information
Legal information is often accessible via portal web sites. Legal documents typically combine structured and unstructured information, the former being tagged with markup languages such as XML (Extensible Markup Language). Current information retrieval research takes into account the structured information content of documents when computing the relevance ranking. Such an approach is very promi...
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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 ...
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In this paper, we propose a novel ranking function learning framework based on relevance propagation. The propagation process is used to propagate the relevance scores from labeled documents to other unlabeled ones so that more training data are available to learn the ranking function. It is realized by the manifold ranking algorithm, which has been proved to be very effective in content-based ...
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Pooling is a document sampling strategy commonly used to collect relevance judgments when multiple retrieval/ranking algorithms are involved. A fixed number of top ranking documents from each algorithm form a pool. Traditionally, expensive experts judge the pool of documents for relevance. We propose and test two hybrid algorithms as alternatives that reduce assessment costs and are effective. ...
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This paper describes the system developed by the Language and Reasoning Group of UAM for the Relevance Feedback track of INEX 2012. The presented system focuses on the problem of ranking documents in accordance to their relevance. It is mainly based on the following hypotheses: (i) current IR machines are able to retrieve relevant documents for most of general queries, but they can not generate...
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