Sara Keretna1, Chee Peng Lim2, Doug Creighton3, Khaled Bashir Shaban4. 1. Centre for Intelligent Systems Research, Deakin University, Australia. Electronic address: skeretna@deakin.edu.au. 2. Centre for Intelligent Systems Research, Deakin University, Australia. Electronic address: chee.lim@deakin.edu.au. 3. Centre for Intelligent Systems Research, Deakin University, Australia. Electronic address: dougc@deakin.edu.au. 4. Computer Science and Engineering Department, College of Engineering, Qatar University, Qatar. Electronic address: khaled.shaban@qu.edu.qa.
Abstract
OBJECTIVE: The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications. METHODS: An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy. RESULTS: The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.
OBJECTIVE: The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications. METHODS: An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy. RESULTS: The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.
Keywords:
Biomedical text annotation; Biomedical text mining; Information extraction; Natural language processing; Unstructured electronic medical records