Identifying non-elliptical entity mentions in a coordinated NP with ellipses.

Named entities in the biomedical domain are often written using a Noun Phrase (NP) along with a coordinating conjunction such as 'and' and 'or'. In addition, repeated words among named entity mentions are frequently omitted. It is often difficult to identify named entities. Although various Named Entity Recognition (NER) methods have tried to solve this problem, these methods can only deal with relatively simple elliptical patterns in coordinated NPs. We propose a new NER method for identifying non-elliptical entity mentions with simple or complex ellipses using linguistic rules and an entity mention dictionary. The GENIA and CRAFT corpora were used to evaluate the performance of the proposed system. The GENIA corpus was used to evaluate the performance of the system according to the quality of the dictionary. The GENIA corpus comprises 3434 non-elliptical entity mentions in 1585 coordinated NPs with ellipses. The system achieves 92.11% precision, 95.20% recall, and 93.63% F-score in identification of non-elliptical entity mentions in coordinated NPs. The accuracy of the system in resolving simple and complex ellipses is 94.54% and 91.95%, respectively. The CRAFT corpus was used to evaluate the performance of the system under realistic conditions. The system achieved 78.47% precision, 67.10% recall, and 72.34% F-score in coordinated NPs. The performance evaluations of the system show that it efficiently solves the problem caused by ellipses, and improves NER performance. The algorithm is implemented in PHP and the code can be downloaded from https://code.google.com/p/medtextmining/.

An MLP-based feature subset selection for HIV-1 protease cleavage site analysis.

OBJECTIVE: In recent years, several machine learning approaches have been applied to modeling the specificity of the human immunodeficiency virus type 1 (HIV-1) protease cleavage domain. However, the high dimensional domain dataset contains a small number of samples, which could misguide classification modeling and its interpretation. Appropriate feature selection can alleviate the problem by eliminating irrelevant and redundant features, and thus improve prediction performance. METHODS: We introduce a new feature subset selection method, FS-MLP, that selects relevant features using multi-layered perceptron (MLP) learning. The method includes MLP learning with a training dataset and then feature subset selection using decompositional approach to analyze the trained MLP. Our method is able to select a subset of relevant features in high dimensional, multi-variate and non-linear domains. RESULTS: Using five artificial datasets that represent four data types, we verified the FS-MLP performance with seven other feature selection methods. Experimental results showed that the FS-MLP is superior at high dimensional, multi-variate and non-linear domains. In experiments with HIV-1 protease cleavage dataset, the FS-MLP selected a set of 14 highly relevant features among 160 original features. On a validation set of 131 test instances, classifiers that used the 14 features showed about 95% accuracy which outperformed other seven methods in terms of accuracy and the number of features. CONCLUSIONS: Our experimental results indicate that the FS-MLP is effective in analyzing multi-variate, non-linear and high dimensional datasets such as HIV-1 protease cleavage dataset. The 14 relevant features which were selected by the FS-MLP provide us with useful insights into the HIV-1 cleavage site domain as well. The FS-MLP is a useful method for computational sequence analysis in general.