Improving clinical named entity recognition in Chinese using the graphical and phonetic feature
| Autor: | Sophia Ananiadou, Yifei Wang, Jun'ichi Tsujii |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Text mining
020205 medical informatics Computer science Health Informatics 02 engineering and technology English language lcsh:Computer applications to medicine. Medical informatics computer.software_genre Machine Learning Named-entity recognition Phonetics 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Electronic Health Records Humans Chinese language Data Curation Language Natural Language Processing Artificial neural network business.industry Research Health Policy Pinyin Semantics Computer Science Applications Named entity recognition lcsh:R858-859.7 Embedding 020201 artificial intelligence & image processing Artificial intelligence Chinese characters business computer Neural networks Natural language processing |
| Zdroj: | BMC Medical Informatics and Decision Making BMC Medical Informatics and Decision Making, Vol 19, Iss S7, Pp 1-7 (2019) |
| ISSN: | 1472-6947 |
| Popis: | BackgroundClinical Named Entity Recognition is to find the name of diseases, body parts and other related terms from the given text. Because Chinese language is quite different with English language, the machine cannot simply get the graphical and phonetic information form Chinese characters. The method for Chinese should be different from that for English. Chinese characters present abundant information with the graphical features, recent research on Chinese word embedding tries to use graphical information as subword. This paper uses both graphical and phonetic features to improve Chinese Clinical Named Entity Recognition based on the presence of phono-semantic characters.MethodsThis paper proposed three different embedding models and tested them on the annotated data. The data have been divided into two sections for exploring the effect of the proportion of phono-semantic characters.ResultsThe model using primary radical and pinyin can improve Clinical Named Entity Recognition in Chinese and get the F-measure of 0.712. More phono-semantic characters does not give a better result.ConclusionsThe paper proves that the use of the combination of graphical and phonetic features can improve the Clinical Named Entity Recognition in Chinese. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |