A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text
المؤلفون المشاركون
Fanany, Mohamad Ivan
Sadikin, Mujiono
Basaruddin, T.
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-10-24
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
One essential task in information extraction from the medical corpus is drug name recognition.
Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name.
Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75).
This paper presents a new treatment in data representation techniques to overcome some of those challenges.
We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training.
The first technique is evaluated with the standard NN model, that is, MLP.
The second technique involves two deep network classifiers, that is, DBN and SAE.
The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM.
In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sadikin, Mujiono& Fanany, Mohamad Ivan& Basaruddin, T.. 2016. A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099658
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sadikin, Mujiono…[et al.]. A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-16.
https://search.emarefa.net/detail/BIM-1099658
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sadikin, Mujiono& Fanany, Mohamad Ivan& Basaruddin, T.. A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099658
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1099658
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر