Ontology-Based Categorization of Bacteria and Habitat Entities
            using Information Retrieval Techniques

Tiftikci, Mert; Şahin, H. Bahar; Büyüköz, Berfu; Yayıkçı, Alper; Özgür, Arzucan

doi:10.18653/v1/w16-3007

Cited by 7 publications

(9 citation statements)

References 9 publications

(5 reference statements)

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“…However, our system is able to outperform BOUN (Tiftikci et al, 2016), the winning system from the BioNLP'16 BB3 Shared Task, by 1pp, 1.5pp and 1.2pp on HABITAT, BACTERIA and all entities respectively.…”

Section: Categorizationmentioning

confidence: 79%

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End-to-End System for Bacteria Habitat Extraction

Mehryary¹,

Hakala²,

Kaewphan³

et al. 2017

BioNLP 2017

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We introduce an end-to-end system capable of named-entity detection, normalization and relation extraction for extracting information about bacteria and their habitats from biomedical literature. Our system is based on deep learning, CRF classifiers and vector space models. We train and evaluate the system on the BioNLP 2016 Shared Task Bacteria Biotope data. The official evaluation shows that the joint performance of our entity detection and relation extraction models outperforms the winning team of the Shared Task by 19pp on F-score, establishing a new top score for the task. We also achieve state-ofthe-art results in the normalization task.Our system is open source and freely available at https://github.com/ TurkuNLP/BHE.

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Section: Categorizationmentioning

confidence: 79%

“…This task is commonly known as named entity normalization or entity linking and various approaches ranging from Levenshtein edit distances to recurrent neural networks have been suggested as the plausible solutions (Tiftikci et al, 2016;Limsopatham and Collier, 2016).…”

Section: Named Entity Categorizationmentioning

confidence: 99%

End-to-End System for Bacteria Habitat Extraction

Mehryary¹,

Hakala²,

Kaewphan³

et al. 2017

BioNLP 2017

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“…For each mention, the concept with the most similar label is predicted. Machine learning is not required beyond a simple 1-nearest-neighbor algorithm: a cosine similarity measure can be used to detect form similarities between both expressions [21,22] or to handle linguistic variations [23]. However, these approaches have limitations.…”

Section: Static Word Vector-based Methodsmentioning

confidence: 99%

C-Norm: a neural approach to few-shot entity normalization

et al. 2020

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Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics. Results Our approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules. Conclusions Our results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems.

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“…Later, heuristic rules were incorporated to improve the performance (Hanisch, Fundel et al 2005, Kang, Singh et al 2012, Karadeniz and Özgür 2013, Tiftikci, Şahin et al 2016, Cho, Choi et al 2017 presented a NER and normalization joint system utilizing semi-markov models, and it has been adopted by an integrated bioconcept annotation and retrieval platform developed by NIH (Wei, Allot et al 2019). However, many of the studies achieved good performance yet were limited for further improvements due to the common drawbacks of rule-based methods.…”

Section: Related Workmentioning

confidence: 99%

An ensemble CNN method for biomedical entity normalization

Deng¹,

Chen²,

Huang³

et al. 2019

Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

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Different representations of the same concept could often be seen in scientific reports and publications. Entity normalization (or entity linking) is the task to match the different representations to their standard concepts. In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries 1 . The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.

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Ontology-Based Categorization of Bacteria and Habitat Entities using Information Retrieval Techniques

Cited by 7 publications

References 9 publications

End-to-End System for Bacteria Habitat Extraction

End-to-End System for Bacteria Habitat Extraction

C-Norm: a neural approach to few-shot entity normalization

An ensemble CNN method for biomedical entity normalization

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