عرض بسيط للتسجيلة

المؤلفKeretna, Sara
المؤلفLim, Chee Peng
المؤلفCreighton, Doug
المؤلفShaban, Khaled Bashir
تاريخ الإتاحة2022-12-21T10:01:46Z
تاريخ النشر2014
اسم المنشورConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/smc.2014.6974324
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37494
الملخصAn accurate Named Entity Recognition (NER) is important for knowledge discovery in text mining. This paper proposes an ensemble machine learning approach to recognise Named Entities (NEs) from unstructured and informal medical text. Specifically, Conditional Random Field (CRF) and Maximum Entropy (ME) classifiers are applied individually to the test data set from the i2b2 2010 medication challenge. Each classifier is trained using a different set of features. The first set focuses on the contextual features of the data, while the second concentrates on the linguistic features of each word. The results of the two classifiers are then combined. The proposed approach achieves an f-score of 81.8%, showing a considerable improvement over the results from CRF and ME classifiers individually which achieve f-scores of 76% and 66.3% for the same data set, respectively. 2014 IEEE.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعBiomedical named entity recognition
Conditional random field
Information extraction
Machine learning
Maximum entropy
Medical text mining
العنوانClassification ensemble to improve medical named entity recognition
النوعConference Paper
الصفحات2630-2636
رقم العددJanuary
رقم المجلد2014-January
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة