Automatic Detection of Pseudo-codes in Scholarly Documents Using Machine Learning

A significant number of scholarly articles in computer science and other disciplines contain algorithms that provide concise descriptions for solving a wide variety of computational problems. For example, Dijkstra’s algorithm describes how to find the shortest paths between two nodes in a graph. Automatic identification and extraction of these algorithms from scholarly digital documents would help enable automatic algorithm indexing, searching, analysis and discovery. An algorithm search engine, which identifies pseudo-codes in scholarly documents and makes them searchable, has been implemented as a part of CiteSeer suite. Here, we illustrate the limitations of the start-of-the-art rule-based pseudo-code detection approach, and present a novel set of machine learning based techniques that extend the previous method.