Classifying Computer Science Papers
This paper addresses the problem of classifying academic papers. It is a building block in constructing an advanced scholarly search engine, such as in crawling and recommending papers in a particular area. Our goal is to identify the best classification method for scholarly data, to choose appropriate parameters, and to gauge how accurate academic papers can be classified using document content only. In addition, we also want to find out whether the neural network approach, which has been proven very successful in many other areas, can help in this particular problem. Our experiments are conducted on 160,000 papers from arXiv data set, each is already labeled as either a computer science (CS) paper or a paper in other areas. We experimented with a variety of classification methods, including Multinomial Naive Bayes on unigram and bigram models, and Logistic Regression on distributional representation obtained from sentence2vec. We find that computer science papers can be identified with high accuracy (F1 close to 0.95) The best method is the bigram model using Multinomial Naive Bayes method and point wise mutual information (PMI) as the feature selection method.