1,907 research outputs found
Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1
Comparative Analysis of Different Data Representations for the Task of Chemical Compound Extraction
Chemical Compound Extraction refers to the task of recognizing chemical instances such as oxygen nitrogen and others. The majority of studies that addressed the task of chemical compound extraction used machine-learning techniques. The key challenge behind using machine-learning techniques lies in employing a robust set of features. In fact, the literature shows that there are numerous types of features used in the task of chemical compound extraction. Such dimensionality of features can be determined via data representation. Some researchers have used N-gram representation for biomedical-named entity recognition, where the most significant terms are represented as features. Meanwhile, others have used detailed-attribute representation in which the features are generalized. As a result, identifying the best combination of features to yield high-accuracy classification becomes challenging. This paper aims to apply the Wrapper Subset Selection approach using two data representations—N-gram and detailed-attributes. Since each data representation would suit a specific classification algorithm, two classifiers were utilized—Naïve Bayes (for detailed-attributes) and Support Vector Machine (for N-gram). The results show that the application of feature selection using detailed-attributes outperformed that of N-gram representation by achieving a 0.722 f-measure. Despite the higher classification accuracy, the selected features using detailed-attribute representation have more meaning and can be applied for further datasets
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