Nazli Goharian

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Prior efforts have shown that under certain situations, retrieval effectiveness may be improved via the use of data fusion techniques. Although these improvements have been observed from the fusion of result sets from several distinct information retrieval systems, it has often been thought that fusing different document retrieval strategies in a single(More)
We propose a novel approach that identifies web page templates and extracts the unstructured data. Extracting only the body of the page and eliminating the template increases the retrieval precision for the queries that generate irrelevant results. We believe that by reducing the number of irrelevant results; the users are encouraged to go back to a given(More)
Many prior efforts have been devoted to the basic idea that data fusion techniques can improve retrieval effectiveness. Recent work in the area suggests that many approaches, particularly multiple-evidence combinations, can be a successful means of improving the effectiveness of a system. Unfortunately, the conditions favorable to effectiveness improvements(More)
One of the tasks a Clinical Decision Support (CDS) system is designed to solve is retrieving the most relevant and actionable literature for a given medical case report. In this work, we present a query reformulation approach that addresses the unique formulation of case reports, making them suitable to be used on a general purpose search engine.(More)
In this work, we emphasize how to merge and re-rank contextual suggestions from the open Web based on a user‟s personal interests. We retrieve relevant results from the open Web by identifying context-independent queries, combining them with location information, and issuing the combined queries to multiple Web search engines. Our learning to rank model(More)
We automatically extract adverse drug reactions (ADRs) from consumer reviews provided on various drug social media sites to identify adverse reactions not reported by the United States Food and Drug Administration (FDA) but touted by consumers. We utilize various lexicons, identify patterns, and generate a synonym set that includes variations of medical(More)
Information Retrieval calls for accurate web page data extraction. To enhance retrieval precision, irrelevant data such as navigational bar and advertisement should be identified and removed prior to indexing. We propose a novel approach that identifies the web page templates and extracts the unstructured data. Our experimental results on several different(More)
Keeping current given the vast volume of medical literature published yearly poses a serious challenge for medical professionals. Thus, interest in systems that aid physicians in making clinical decisions is intensifying. A task of Clinical Decision Support (CDS) systems is retrieving highly relevant medical literature that could help healthcare(More)