Fast Top-k Area Topics Extraction with Knowledge Base
Abstract
What are the most popular research topics in Artificial Intelligence (AI)? We formulate the problem as extracting top- topics that can best represent a given area with the help of knowledge base. We theoretically prove that the problem is NP-hard and propose an optimization model, FastKATE, to address this problem by combining both explicit and latent representations for each topic. We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas. We develop a fast heuristic algorithm to efficiently solve the problem with a provable error bound. We evaluate the proposed model on three real-world datasets. Experimental results demonstrate our model's effectiveness, robustness, real-timeness (return results in s), and its superiority over several alternative methods.
Cite
@article{arxiv.1710.04822,
title = {Fast Top-k Area Topics Extraction with Knowledge Base},
author = {Fang Zhang and Xiaochen Wang and Jingfei Han and Jie Tang and Shiyin Wang and Marie-Francine Moens},
journal= {arXiv preprint arXiv:1710.04822},
year = {2017}
}