{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:49Z","timestamp":1740202069732,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>With the continuous growth of freely accessible knowledge bases and the heterogeneity of textual corpora, selecting the most adequate knowledge base for named entity recognition is becoming a challenge in itself. In this paper, we propose an unsupervised method to rank knowledge bases according to their adequacy for the recognition of named entities in a given corpus. Building on a state-of-the-art, unsupervised entity linking approach, we propose several evaluation metrics to measure the lexical and structural adequacy of a knowledge base for a given corpus. We study the correlation between these metrics and three standard performance measures: precision, recall and F1 score. Our multi-domain experiments on 9 different corpora with 6 knowledge bases show that three of the proposed metrics are strong performance predictors having 0.62 to 0.76 Pearson correlation with precision and 0.96 correlation with both recall and F1 score.<\/jats:p>","DOI":"10.3233\/978-1-61499-672-9-1248","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:05Z","timestamp":1740133625000},"source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Ranking of Knowledge Bases for Named Entity Recognition"],"prefix":"10.3233","author":[{"family":"Mrabet Yassine","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Kilicoglu Halil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Demner-Fushman Dina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2016"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:16:12Z","timestamp":1740136572000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-671-2&spage=1248&doi=10.3233\/978-1-61499-672-9-1248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-672-9-1248","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}