Recent research tends to address the biomedical entity linking problem in a unified framework solely based on surface form matching between mentions and entities. Specifically, these methods focus on addressing thevarietychallenge of the heterogeneous naming of biomedical concepts. Yet, theambiguitychallenge that the same word under different contexts can be used to refer to distinct concepts is usually ignored. To address this challenge, we propose BioPRO, a two-stage entity linking algorithm to enhance the biomedical entity representations based on context-infused prompt learning. The first stage includes a coarse-grained retrieval from a representation space defined by a bi-encoder that independently embeds the mention and entity's surface forms. Unlike previous one-model-fits-all systems, each candidate is then re-ranked with a fine-grained encoder based on prompt-tuning that sufficiently stimulates knowledge in contextual information of mentions and entities. Furthermore, the trained fine-grained encoder can be utilized to generate deep representations of bio-entities and boost candidate retrieval in the first stage. Extensive experiments show that our model achieves promising performance improvements compared with several state-of-the-art (SOTA) techniques on 4 biomedical corpora. We also observe by cases that the proposed context-infused prompt-tuning strategy is effective in solving both thevarietyandambiguitychallenges in the linking task.

Multilingual biomedical entity linking (MBEL) aims to map language-specific mentions in the biomedical text to standardized concepts in a multilingual knowledge base (KB) such as Unified Medical Language System (UMLS).In this paper, we propose Con2GEN, a prompt-based controllable contrastive generation framework for MBEL, which summarizes multidimensional information of the UMLS concept mentioned in biomedical text into a natural sentence following a predefined template.Instead of tackling the MBEL problem with a discriminative classifier, we formulate it as a sequence-tosequence generation task, which better exploits the shared dependencies between source mentions and target entities.Moreover, Con2GEN matches against UMLS concepts in as many languages and types as possible, hence facilitating cross-information disambiguation.Extensive experiments show that our model achieves promising performance improvements compared with several state-of-the-art techniques on the XL-BEL and the Mantra GSC datasets spanning 12 typologically diverse languages.

Biomedical entity linking aims to map mentions in biomedical text to standardized concepts or entities in a curated knowledge base (KB) such as Unified Medical Language System (UMLS). The latest research tends to solve this problem in a unified framework solely based on surface form matching between mentions and entities. Specifically, these methods focus on addressing the variety challenge of the heterogeneous naming of biomedical concepts. Yet, the ambiguity challenge that the same word under different contexts may refer to distinct entities is usually ignored. To address this challenge, we propose a two-stage linking algorithm to enhance the entity representations based on prompt learning. The first stage includes a coarser-grained retrieval from a representation space defined by a bi-encoder that independently embeds the mention and entity’s surface forms. Unlike previous one-model-fits-all systems, each candidate is then re-ranked with a finer-grained encoder based on prompt-tuning that utilizes the contextual information. Extensive experiments show that our model achieves promising performance improvements compared with several state-of-the-art techniques on the largest biomedical public dataset MedMentions and the NCBI disease corpus. We also observe by cases that the proposed prompt-tuning strategy is effective in solving both the variety and ambiguity challenges in the linking task.

OBJECTIVE: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biomedical relations using deep learning and reinforcement learning. MATERIALS AND METHODS: In this study, a new computational model called piecewise attentive convolutional neural network and reinforcement learning (PACNN+RL) was proposed to perform RE on distantly supervised data generated from Unified Medical Language System with MEDLINE abstracts and benchmark datasets. In PACNN+RL, PACNN was introduced to encode semantic information of biomedical text, and the RL method with memory backtracking mechanism was leveraged to alleviate the erroneous data issue. Extensive experiments were conducted on 4 biomedical RE tasks. RESULTS: The proposed PACNN+RL model achieved competitive performance on 8 biomedical corpora, outperforming most baseline systems. Specifically, PACNN+RL outperformed all baseline methods with the F1-score of 0.5592 on the may-prevent dataset, 0.6666 on the may-treat dataset, and 0.3838 on the DDI corpus, 2011. For the protein-protein interaction RE task, we obtained new state-of-the-art performance on 4 out of 5 benchmark datasets. CONCLUSIONS: The performance on many distantly supervised biomedical RE tasks was substantially improved, primarily owing to the denoising effect of the proposed model. It is anticipated that PACNN+RL will become a useful tool for large-scale RE and other downstream tasks to facilitate biomedical knowledge acquisition. We also made the demonstration program and source code publicly available at http://112.74.48.115:9000/.