Wide-scope biomedical named entity recognition and normalization with CRFs, fuzzy matching and character level modeling

Kaewphan, Suwisa; Hakala, Kai; Miekka, Niko; Salakoski, Tapio; Ginter, Filip

doi:10.1093/database/bay096

Cited by 16 publications

(11 citation statements)

References 26 publications

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“…However, traditional ML-based methods need extensive feature engineering, which is time-consuming and labor intensive. Based on their previous work [29], Kaewphan et al [30] further developed a BiLSTM-CRF based model, which used character embeddings learned by a Convolutional Neural Network (CNN) and the predictions from their original NERsuite model [29] as inputs of the recognition model. Neural network-based methods bring significant improvement in PNER performance (3.2% F1score improvement under strict criteria than before).…”

Section: Discussionmentioning

confidence: 99%

“…Typically, hand-crafted rules are clear and effective, but they are inflexible and hard to expand to a new dataset. Kaewphan et al [30] used the same method as their previous work [29] to perform PNEN, but based on their new recognition method. Compared with their previous results, they achieved 1.8% micro-averaged F1-score improvement from 0.397 to 0.415.…”

Section: Comparison With Related Work For Pnenmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Comparison With Related Work For Pnenmentioning

confidence: 99%

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

et al. 2020

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“…There are methods aimed at NER that have been developing during the last years (Kaewphan et al, 2018 ; Korvigo et al, 2018 ; Hemati and Mehler, 2019 ; Hong and Lee, 2020 ; Huang et al, 2020 ; Kilicoglu et al, 2020 ). Most of them are based on algorithms for NER related either to chemicals or biological objects.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

et al. 2020

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Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.

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“…Zero-Shot Linking with Approximate Dictionary Matching. Using Sim-String [10], we represent our restricted set of aliases from UMLS as character n-grams, similarly to previous works [11,12]. After experimenting with different sizes, we found that char n-grams of size 3 performed best.…”

Section: Solutionmentioning

confidence: 78%

MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching

Loureiro

Jorge

2020

Lecture Notes in Computer Science

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Progress in the field of Natural Language Processing (NLP) has been closely followed by applications in the medical domain. Recent advancements in Neural Language Models (NLMs) have transformed the field and are currently motivating numerous works exploring their application in different domains. In this paper, we explore how NLMs can be used for Medical Entity Linking with the recently introduced MedMentions dataset, which presents two major challenges: (1) a large target ontology of over 2M concepts, and (2) low overlap between concepts in train, validation and test sets. We introduce a solution, MedLinker, that addresses these issues by leveraging specialized NLMs with Approximate Dictionary Matching, and show that it performs competitively on semantic type linking, while improving the state-of-the-art on the more fine-grained task of concept linking (+4 F1 on MedMentions main task).

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Wide-scope biomedical named entity recognition and normalization with CRFs, fuzzy matching and character level modeling

Cited by 16 publications

References 26 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

Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies

MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching

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