184,141 research outputs found
Bert, C A, VX54408
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/371578Surname: BERT
Given Name(s) or Initials: C A
Military Service Number or Last Known Location: VX54408
Missing, Wounded and Prisoner of War Enquiry Card Index Number: 45802182073
Item: [2016.0049.03905] "Bert, C A, VX54408
Oral History Interview with Bert Dawson, March 18, 2010
No full textThe National Museum of the Pacific War presents an interview with Bert Dawson. Dawson joined the Marine Corps in 1943. He served with the 5th Marine Division, as a paratrooper. He participated in the Battle of Iwo Jima. From Iwo, Dawson traveled to Sasebo, Japan. He was discharged in 1945
Germinação de Stevia rebaudiana Bert
No full textDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia
C-BERT: A Mongolian reverse dictionary based on fused lexical semantic clustering and BERT
No full textA reverse dictionary is an electronic dictionary that accepts user-provided natural language descriptions and returns semantically matching lexicons. Despite substantial research achievements in Mongolian lexicography, discussions on Mongolian reverse dictionaries have not yet emerged. To address this, we propose an innovative model, C-BERT, combining advanced lexical semantic clustering and BERT classification technology. Initially, the K-means algorithm was used to cluster preprocessed entries from well-known Mongolian dictionaries into 5000 clusters, forming a comprehensive training set. We then optimized this training set’s data distribution through random negative sampling and fine-tuned the CINO-large model, leading to the creation of the C-BERT model. When users submit descriptions, C-BERT matches them with the central words of 5000 clusters, selecting the top 125 clusters. It then matches target words within these clusters to recommend the top 100 semantically relevant candidates. Compared to the seven baseline models, C-BERT demonstrates superior performance, particularly when evaluated on datasets with human-generated descriptions, where its synonym accuracy@10/100 reaches 16.5% and 71%, respectively. Benefiting from clustering, C-BERT improves inference speed more than tenfold, significantly enhancing its practical utility. Accordingly, we have developed a user-friendly online application platform based on C-BERT for a broad range of users, available at http://mrdp.net/
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