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Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data
Genomics & Informatics ; : e20-2019.
Article in English | WPRIM | ID: wpr-763804
ABSTRACT
Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.
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Full text: Available Index: WPRIM (Western Pacific) Main subject: Semantics / Vocabulary / Information Storage and Retrieval / Dataset / Methods Language: English Journal: Genomics & Informatics Year: 2019 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Semantics / Vocabulary / Information Storage and Retrieval / Dataset / Methods Language: English Journal: Genomics & Informatics Year: 2019 Type: Article