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But much work in text analytics focuses on helping people investigate other textual units, such as events, clusters, ranked documents, entity relationships, or thematic hierarchies. Informed by a study into the needs of historians and archivists, we thus propose\n            <jats:sc>ClioQuery<\/jats:sc>\n            , a text analytics system uniquely organized around the analysis of query words in context.\n            <jats:sc>ClioQuery<\/jats:sc>\n            \u00a0applies text simplification techniques from natural language processing to help historians quickly and comprehensively gather and analyze all occurrences of a query word across an archive. It also pairs these new NLP methods with more traditional features like linked views and in-text highlighting to help engender trust in summarization techniques. We evaluate\n            <jats:sc>ClioQuery<\/jats:sc>\n            \u00a0with two separate user studies, in which historians explain how\n            <jats:sc>ClioQuery<\/jats:sc>\n            \u2019s novel text simplification features can help facilitate historical research. We also evaluate with a separate quantitative comparison study, which shows that\n            <jats:sc>ClioQuery<\/jats:sc>\n            \u00a0helps crowdworkers find and remember historical information. Such results suggest possible new directions for text analytics in other query-oriented settings.\n          <\/jats:p>","DOI":"10.1145\/3524025","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T11:37:31Z","timestamp":1651232251000},"page":"1-49","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["<scp>ClioQuery<\/scp>\n            : Interactive Query-oriented Text Analytics for Comprehensive Investigation of Historical News Archives"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8663-2628","authenticated-orcid":false,"given":"Abram","family":"Handler","sequence":"first","affiliation":[{"name":"Department of Information Science, University of Colorado, Boulder, CO, USA"}]},{"given":"Narges","family":"Mahyar","sequence":"additional","affiliation":[{"name":"Manning College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA"}]},{"given":"Brendan","family":"O\u2019Connor","sequence":"additional","affiliation":[{"name":"Manning College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"VAST","author":"Alexander Eric","year":"2014","unstructured":"Eric Alexander, Joe Kohlmann, Robin Valenza, Michael Witmore, and Michael Gleicher. 2014. 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