Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of “what should one learn first,”we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

Alexander Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Weitai Ting, Robert Tung, Caitlin Westerfield, Dragomir Radev. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.

We introduce and theoretically study the convergence behavior of iterative matrix correlation computation and show how it can be leveraged to derive a novel bisection clustering algorithm with unique characteristics. A correlation matrix is a symmetric n × n matrix for n vectors, where the (i, j)-th entry is the Pearson correlation coefficient between vectors i and j. We observe that in general cases iterative update of the correlation matrix leads to its convergence where all entries are either 1 or -1. Moreover, the same convergence behavior holds if we select a pre-determined subset of columns from the correlation matrix in each iteration for the next iteration of correlation computation. We mathematically prove this observation, analyze its convergence behavior, and propose an efficiency improvement technique. While this observation is significant in its own right and may have many applications, we focus on how to apply it to achieve bisection clustering. Clustering is a fundamental data mining task. Bisection clustering is particularly important because it can be used as a building block to construct hierarchical clustering and arbitrary k-clustering. The derived algorithm, which we call Corbis, works in a fundamentally different way from existing ones with preferable characteristics and comparative advantages especially for high dimensional data. It can be an important addition to the arsenal of clustering algorithms.