Medical Entity Recognition using Conditional Random Field (CRF)

Autor:	Raditya Herwando, Mirna Adriani, Meganingrum Arista Jiwanggi
Předmět:	Conditional random field Phrase Computer science business.industry Supervised learning computer.software_genre Task (project management) Support vector machine Named-entity recognition Task analysis Artificial intelligence business computer Word (computer architecture) Natural language processing
Zdroj:	Web of Science IWBIS
Popis:	The main objective of this research is to extract the health information, such as diseases, symptoms, treatments and drugs from the health online forum discussion. The task is referred as the medical entity recognition (MER) in which is defined as the Named Entity Recognition (NER) task to extract the information from the unstructured text and transform it into the structured forms in the health field. The approach for the task used in this research is a supervised learning using Conditional Random Field(CRF). We experimented several combinations of features in order to produce the results with the best accuracy. As the final result, this research obtained the best accuracy of precision 70.97%, recall 57.83%, and f-measures 63.69%. The best combination of features resulting the best overall result consists of the word itself, phrase, dictionary, the first preceding word and the word length.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c3851706c527c739bdbf799525b096d https://publons.com/wos-op/publon/55992469/ Zobrazit plný text záznamu