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As previously stated, it is now well-known that machine learning is a set of algorithms that can be used to derive knowledge from data, and that they have been successfully adopted in a variety of domains (Brooks and Steiner 2003). Due to the development of powerful machine learning algorithms, as well as an increasing interest in this field, there are numerous works on the subject. However, most of them focus on the application of machine learning to problems which are previously known and have been successfully solved. Acknowledging the difficulty in applying machine learning to a new problem, this work develops an approach that combines learning and knowledge engineering to solve a previously unsolved problem. Starting from questions from Stack Overflow, we construct and annotate a knowledge base to constrain the search space of a question-answering system in such a way that answering the question requires no external knowledge. We then train a machine learning model to learn from question-answering data, and use it to evaluate the automatic construction of the knowledge base. The knowledge base is then used to answer a new question. This approach has three major components: (1) automatically learning from questions and answers, (2) automatically constructing the knowledge base, and (3) answering a new question. We had initially proposed to use a hybrid system combining learning approaches and expert systems. In this paper, we report our modifications to keep the knowledge acquisition system as independent as possible. To evaluate some of the different learning algorithms, we have run some experiments on a DBLP dataset comprising approximately 90,000 publications. We compare the results with the manual analysis of the literature, and conclude that this new approach is competitive. However, future work will focus on the development of new algorithms as well as on the application of the proposed approach to different domains.
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