CNN-based ranking for biomedical entity normalization

Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Dong, Hongbiao

doi:10.1186/s12859-017-1805-7

Cited by 104 publications

(84 citation statements)

References 11 publications

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“…Later, some work [11,12] proposed to convert mentions and candidate entities into a common vector space, and then disambiguated candidate entities by a scoring function (e.g., cosine similarity). In recent years, neural network-based approaches have shown considerable success in entity normalization [13][14][15]. These methods used neural architectures to learn the context representations around an entity mention and calculated the context-entity similarity scores to determine which candidate is a correct assignment.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes

et al. 2020

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Background: Automated biomedical named entity recognition and normalization serves as the basis for many downstream applications in information management. However, this task is challenging due to name variations and entity ambiguity. A biomedical entity may have multiple variants and a variant could denote several different entity identifiers. Results: To remedy the above issues, we present a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN). On one hand, a large amount of entity name knowledge extracted from biomedical knowledge bases is used to recognize more entity variants. On the other hand, structural knowledge of entities is extracted and encoded as identifier (ID) embeddings, which are then used for better entity normalization. Moreover, deep contextualized word representations generated by pre-trained language models are also incorporated into our knowledge-enhanced system for modeling multi-sense information of entities. Experimental results on the BioCreative VI Bio-ID corpus show that our proposed knowledge-enhanced system achieves 0.871 F1-score for PNER and 0.445 F1-score for PNEN, respectively, leading to a new state-of-the-art performance. Conclusions: We propose a knowledge-enhanced system that combines both entity knowledge and deep contextualized word representations. Comparison results show that entity knowledge is beneficial to the PNER and PNEN task and can be well combined with contextualized information in our system for further improvement.

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Section: Introductionmentioning

confidence: 99%

Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes

et al. 2020

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“…Abstracts Total Unique Training set 692 5932 1538 Test set 100 960 427 Total 792 6892 1965 Table 1: NCBI Disease Corpus Statistics domain sub-word level information 2 in solving the task of disease normalization. 3) Unlike existing systems (D'Souza and Ng, 2015), (Li et al, 2017), we present a robust and portable candidate generation approach without making use of external resources or hand-engineered sieves to deal with morphological variations. Our system achieves state-of-the-art performance on NCBI disease dataset (Dogan et al, 2014) 2 Dataset…”

Section: Datasetmentioning

confidence: 99%

“…Accuracy (D'Souza and Ng, 2015) 84.65 (Li et al, 2017) 86 We choose the evaluation measure as accuracy. Since, the highest similar candidate is of primary interest in the task of entity linking, so we choose the top-K ( Where K = 1).…”

Section: Model Namementioning

confidence: 99%

Medical Entity Linking using Triplet Network

Mondal¹,

Purkayastha²,

Sarkar³

et al. 2019

Proceedings of the 2nd Clinical Natural Language Processing Workshop

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Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.

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“…Besides the bacteria biotopes, there exist a significant amount of prior work on biomedical named entity normalization for different types of biomedical entities including genes/proteins (Morgan et al, 2008;Wermter et al, 2009;Lu et al, 2011; and diseases (Leaman et al, 2013;Li et al, 2017). However, the need for manually annotated training data makes the adaptation of such methods to new entities difficult.…”

Section: Named Entity Normalizationmentioning

confidence: 99%

BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes

Karadeniz

Tuna²,

Özgür

2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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This paper presents our participation at the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.

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CNN-based ranking for biomedical entity normalization

Cited by 104 publications

References 11 publications

Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes

Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes

Medical Entity Linking using Triplet Network

BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes

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