Bert-Er

Chatterjee, Shubham; Dietz, Laura

doi:10.1145/3477495.3531944

Cited by 16 publications

(1 citation statement)

References 37 publications

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“…Experimental results on several IR benchmarks show that ARES, PROP, Webformer, and HARP perform significantly better than traditional methods such as BM25 after fine-tuning. Also, some researchers explore incorporating structure information for entity retrieval (Gerritse, Hasibi, and de Vries 2020;Nikolaev and Kotov 2020;Chatterjee and Dietz 2022;Gerritse, Hasibi, and de Vries 2022).…”

Section: Pre-training Methods Tailored For Irmentioning

confidence: 99%

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-Hoc Retrieval

Su,

Ai,

et al. 2024

AAAI

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With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems. Benefiting from the pre-training and fine-tuning paradigm, these models achieve state-of-the-art performance. In previous works, plain texts in Wikipedia have been widely used in the pre-training stage. However, the rich structured information in Wikipedia, such as the titles, abstracts, hierarchical heading (multi-level title) structure, relationship between articles, references, hyperlink structures, and the writing organizations, has not been fully explored. In this paper, we devise four pre-training objectives tailored for IR tasks based on the structured knowledge of Wikipedia. Compared to existing pre-training methods, our approach can better capture the semantic knowledge in the training corpus by leveraging the human-edited structured data from Wikipedia. Experimental results on multiple IR benchmark datasets show the superior performance of our model in both zero-shot and fine-tuning settings compared to existing strong retrieval baselines. Besides, experimental results in biomedical and legal domains demonstrate that our approach achieves better performance in vertical domains compared to previous models, especially in scenarios where long text similarity matching is needed. The code is available at https://github.com/oneal2000/Wikiformer.

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Section: Pre-training Methods Tailored For Irmentioning

confidence: 99%

Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-Hoc Retrieval

Su,

Ai,

et al. 2024

AAAI

View full text Add to dashboard Cite

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Potential for artificial intelligence in medicine and its application to male infertility

Kobayashi

2024

Reprod Medicine & Biology

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BackgroundThe third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production.MethodsThis review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility.Main Findings (Results)In research on male infertility, I established an AI‐based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non‐obstructive azoospermia, both by no‐code AI.ConclusionsAI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No‐code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.

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