{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:15Z","timestamp":1777854495831,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2014,7,31]],"date-time":"2014-07-31T00:00:00Z","timestamp":1406764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2014,10]]},"abstract":"<jats:p>Classifying documents to a large-scale web taxonomy is a challenging research problem because of a large number of categories and associated documents in the taxonomy. The state-of-the-art solution known as the narrow-down approach utilizes a search engine to reduce an entire category hierarchy to most relevant categories and selects the best one among them using a classifier. In a recent language modelling approach, top-level category information (or global information) was used in judging the appropriateness of a local category, which led to performance improvements. However, we observe that using global information has a limited influence on the final category selection under some conditions. First, global information may be inaccurate even though it is generated by a top-level category classifier using an entire hierarchy. Second, it has little influence when two competing categories share the same top-level category or when the local category information has too strong an influence on the final category selection. To resolve the limitations, in this paper, we propose two external methods: (1) a meta-classifier with novel dependency features among top-level categories based on an ensemble learning framework; and (2) a query modification model based on a statistical feedback method to improve the query document representation instead of just juggling with information in the hierarchy. Our methods were evaluated using the Open Directory Project test collection.<\/jats:p>","DOI":"10.1177\/0165551514544626","type":"journal-article","created":{"date-parts":[[2014,7,31]],"date-time":"2014-07-31T21:42:31Z","timestamp":1406842951000},"page":"688-708","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["External methods to address limitations of using global information on the narrow-down approach for hierarchical text classification"],"prefix":"10.1177","volume":"40","author":[{"given":"Heung-Seon","family":"Oh","sequence":"first","affiliation":[{"name":"Korea Institute of Science and Technology Information, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchul","family":"Jung","sequence":"additional","affiliation":[{"name":"Korea Institute of Science and Technology Information, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2014,7,31]]},"reference":[{"key":"bibr1-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277837"},{"key":"bibr2-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526778"},{"key":"bibr3-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1076034.1076120"},{"key":"bibr4-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063768"},{"key":"bibr5-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341564"},{"key":"bibr6-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390440"},{"key":"bibr7-0165551514544626","first-page":"359","volume-title":"Proceedings of the fifteenth international conference on machine learning","author":"McCallum A","year":"1998"},{"key":"bibr8-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1031171.1031186"},{"key":"bibr9-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"bibr10-0165551514544626","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1109\/ICDM.2001.989560","author":"Sun A","year":"2001","journal-title":"ICDM 2001, proceedings IEEE international conference on data mining"},{"key":"bibr11-0165551514544626","doi-asserted-by":"publisher","DOI":"10.1145\/1571941.1571946"},{"key":"bibr12-0165551514544626","unstructured":"H. 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