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Hybrid X-Linker: Automated Data Generation and Extreme Multi-label Ranking for Biomedical Entity Linking

Autor: Ruas, Pedro, Gallego, Fernando, Veredas, Francisco J., Couto, Francisco M.

State-of-the-art deep learning entity linking methods rely on extensive human-labelled data, which is costly to acquire. Current datasets are limited in size, leading to inadequate coverage of biomedical concepts and diminished performance when appli

Externí odkaz: http://arxiv.org/abs/2407.06292

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Querying Triadic Concepts through Partial or Complete Matching of Triples

Autor: Ruas, Pedro Henrique B., Missaoui, Rokia, Ibrahim, Mohamed Hamza

In this paper, we introduce a new method for querying triadic concepts through partial or complete matching of triples using an inverted index, to retrieve already computed triadic concepts that contain a set of terms in their extent, intent, and/or

Externí odkaz: http://arxiv.org/abs/2401.10271

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LASIGE and UNICAGE solution to the NASA LitCoin NLP Competition

Autor: Ruas, Pedro, Sousa, Diana F., Neves, André, Cruz, Carlos, Couto, Francisco M.

Biomedical Natural Language Processing (NLP) tends to become cumbersome for most researchers, frequently due to the amount and heterogeneity of text to be processed. To address this challenge, the industry is continuously developing highly efficient

Externí odkaz: http://arxiv.org/abs/2308.05609

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