Knowledge-rich self-supervised entity linking
WebIn this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. WebDec 15, 2024 · In this paper, we explore Knowledge-RIch Self-Supervision ( K R I S S) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it …
Knowledge-rich self-supervised entity linking
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WebMedical entity linking is the task of identifying and standardizing medical concepts referred to in an unstructured text. Most of the existing methods adopt a three-step approach of … WebJun 26, 2024 · Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas Traditional entity linking systems assume that the schema of the knowledge base that ties the predicted entities together is known. They proposed a new method to convert the schema of unknown entities to BERT embedding using attributes and auxiliary tokens.
WebApr 14, 2024 · Entity linking (EL) aims to find entities that the textual mentions refer to from a knowledge base (KB). The performance of current distantly supervised EL methods is not satisfactory under the ...
WebThis paper explores learning rich self-supervised entity representations from large amounts of associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base completion, question an-swering, and more. Unlike other methods that harvest self-supervision signals based WebKnowledge-Rich Self-Supervised Entity Linking. [ Paper] Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao and …
WebDec 15, 2024 · In this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for entity linking, by leveraging readily available domain knowledge. In training, it generates self …
WebApr 13, 2024 · In recommender system, knowledge graph (KG) is usually leveraged as side information to enhance representation ability, and has been proven to mitigate the cold-start and data sparsity issues. However, due to the complexity of KG construction, it inevitably brings a large amount of noise, thus simply introducing KG into recommender system … cse impiantiWebJul 8, 2024 · The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity … cse immobilier senegalWebApr 1, 2024 · Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision April 2024 Patterns DOI: CC BY 4.0 Authors: Sam Preston Mu... march 1991 chinese zodiacWebKnowledge-Rich Self-Supervision for Biomedical Entity Linking EMNLP 2024 Findings Sheng Zhang* , Hao Cheng * , Shikhar Vashishth * , Cliff Wong , Jinfeng Xiao , Xiaodong Liu , Tristan Naumann , Jianfeng Gao , Hoifung Poon (*equal contribution) [ Code ] Modular Self-Supervision for Document-Level Relation Extraction EMNLP 2024 march 1994 chinese zodiacWebApr 14, 2024 · Entity linking (EL) aims to find entities that the textual mentions refer to from a knowledge base (KB). The performance of current distantly supervised EL methods is … cse imageWebIn this paper, we explore Knowledge-RIch Self-Supervision (KRISS) for entity linking by leverag-ing readily available domain knowledge to compen-sate for the lack of labeled … cse imagerie medicale parisWebIn this paper, we explore Knowledge-RIch Self-Supervision (K R I S S) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self ... cse immatriculation