15,594 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No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete.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
Probing BERT for Ranking Abilities
No full textContextual models like BERT are highly effective in numerous text-ranking tasks. However, it is still unclear as to whether contextual models understand well-established notions of relevance that are central to IR. In this paper, we use probing, a recent approach used to analyze language models, to investigate the ranking abilities of BERT-based rankers. Most of the probing literature has focussed on linguistic and knowledge-aware capabilities of models or axiomatic analysis of ranking models. In this paper, we fill an important gap in the information retrieval literature by conducting a layer-wise probing analysis using four probes based on lexical matching, semantic similarity as well as linguistic properties like coreference resolution and named entity recognition. Our experiments show an interesting trend that BERT-rankers better encode ranking abilities at intermediate layers. Based on our observations, we train a ranking model by augmenting the ranking data with the probe data to show initial yet consistent performance improvements (The code is available at https://github.com/yolomeus/probing-search/ ).Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System
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
BERT Rankers are Brittle: A Study using Adversarial Document Perturbations
No full textContextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under adversarial inputs is under-explored. In this paper, we argue that BERT-rankers are not immune to adversarial attacks targeting retrieved documents given a query. Firstly, we propose algorithms for adversarial perturbation of both highly relevant and non-relevant documents using gradient-based optimization methods. The aim of our algorithms is to add/replace a small number of tokens to a highly relevant or non-relevant document to cause a large rank demotion or promotion. Our experiments show that a small number of tokens can already result in a large change in the rank of a document. Moreover, we find that BERT-rankers heavily rely on the document start/head for relevance prediction, making the initial part of the document more susceptible to adversarial attacks. More interestingly, we find a small set of recurring adversarial words that when added to documents result in successful rank demotion/promotion of any relevant/non-relevant document respectively. Finally, our adversarial tokens also show particular topic preferences within and across datasets, exposing potential biases from BERT pre-training or downstream datasets. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System
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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