Reference number : GC-2017-Q1-01849, amendment number : , agreement type : G, recipient legal name : Grossman, Maura (University of Waterloo), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The objective of high-recall information retrieval (HRIR) is to identify substantially all information relevant to an information need, where the consequences of missing or untimely results may have serious legal, policy, health, social, safety, defence, or financial implications. To find acceptance in practice, HRIR technologies must be more effective&mdash;and must be shown to be more effective&mdash;than current practice, according to the legal, statutory, regulatory, ethical, or professional standards governing the application domain. The applicant is the leading expert in this field, having first recognized the transferability of machine-learning technologies from spam filtering to electronic discovery, and then having established the scientific foundation for their acceptance in civil litigation and regulatory proceedings, both in the United States and Europe. 
 
The proposed research includes experiments seeking to enhance and extend the HRIR results achieved to date, within application domains where it is critical to satisfy an information need when it arises, and on an ongoing basis, by finding substantially all relevant information. In furtherance of these goals, four general avenues of investigation will be pursued: (1) measuring the effectiveness of HRIR technologies in practice using live data and real information needs; (2) modeling more nuanced user-system interactions and employing higher-fidelity evaluation measures; (3) addressing the technological and logistical challenges presented by massive datasets and high-volume data streams; and (4) discovering and evaluating the effectiveness of HRIR technologies in new applications domains. Such domains include, but are not limited to, distinguishing between public and non-public records in the curation of government archives; systematic review for meta-analysis in evidence-based medicine; separating irregularities and intentional misstatements from unintentional errors in accounting restatements; performing &ldquo;due diligence&rdquo; in connection with pending mergers, acquisitions, and financing transactions; and surveillance and compliance activities involving massive datasets. The proposed research promises to establish the effectiveness of HRIR technology over current practice in those domains, as well as others., program purpose french : The objective of high-recall information retrieval (HRIR) is to identify substantially all information relevant to an information need, where the consequences of missing or untimely results may have serious legal, policy, health, social, safety, defence, or financial implications. To find acceptance in practice, HRIR technologies must be more effective&mdash;and must be shown to be more effective&mdash;than current practice, according to the legal, statutory, regulatory, ethical, or professional standards governing the application domain. The applicant is the leading expert in this field, having first recognized the transferability of machine-learning technologies from spam filtering to electronic discovery, and then having established the scientific foundation for their acceptance in civil litigation and regulatory proceedings, both in the United States and Europe. 
 
The proposed research includes experiments seeking to enhance and extend the HRIR results achieved to date, within application domains where it is critical to satisfy an information need when it arises, and on an ongoing basis, by finding substantially all relevant information. In furtherance of these goals, four general avenues of investigation will be pursued: (1) measuring the effectiveness of HRIR technologies in practice using live data and real information needs; (2) modeling more nuanced user-system interactions and employing higher-fidelity evaluation measures; (3) addressing the technological and logistical challenges presented by massive datasets and high-volume data streams; and (4) discovering and evaluating the effectiveness of HRIR technologies in new applications domains. Such domains include, but are not limited to, distinguishing between public and non-public records in the curation of government archives; systematic review for meta-analysis in evidence-based medicine; separating irregularities and intentional misstatements from unintentional errors in accounting restatements; performing &ldquo;due diligence&rdquo; in connection with pending mergers, acquisitions, and financing transactions; and surveillance and compliance activities involving massive datasets. The proposed research promises to establish the effectiveness of HRIR technology over current practice in those domains, as well as others., agreement title english : Evaluation of High-Recall Human-in-the-Loop Information Retrieval Technologies, agreement title french : Evaluation of High-Recall Human-in-the-Loop Information Retrieval Technologies, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada