Fast Large-Scale Approximate Graph Construction for NLP

Amit Goyal, Hal Daumé III, Raul Guerra

Abstract: Many natural language processing problems involve constructing large nearest-neighbor graphs. We propose a system called FLAG to construct such graphs approximately from large data sets. To handle the large amount of data, our algorithm maintains approximate counts based on sketching algorithms. To find the approximate nearest neighbors, our algorithm pairs a new distributed online-PMI algorithm with novel fast approximate nearest neighbor search algorithms (variants of Pleb). These algorithms return the approximate nearest neighbors quickly. We show our system's efficiency in both intrinsic and extrinsic experiments. We further evaluate our fast search algorithms both quantitatively and qualitatively on two NLP applications.