Knowledge discovery through text-based similarity searches for astronomy literature

Journal of Astrophysics and Astronomy

WOLFGANG E. KERZENDORF^1,2¹ 

[1] Center for Cosmology and Particle Physics, New York University, 726 Broadway, New York, NY 10003, USA.^1;European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching, Germany.^2

关键词: Natural language processing;    methods: statistical.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

The increase in the number of researchers coupled with the ease of publishing and distribution of scientific papers (due to technological advancements) has resulted in a dramatic increase in astronomy literature.This has likely led to the predicament that the body of the literature is too large for traditional human consumption and that related and crucial knowledge is not discovered by researchers. In addition to the increased production of astronomical literature, recent decades have also brought several advancements in computational linguistics. Especially, the machine-aided processing of literature dissemination might make it possible to convert this stream of papers into a coherent knowledge set. In this paper, we present the application of computational linguistics techniques to astronomy literature. In particular, we developed a tool that will find similar articles purely based on text content from an input paper. We find that our technique performs robustly in comparisonwith other tools recommending articles given a reference paper (known as recommender system). Our novel tool shows great power in combining computational linguistics with astronomy literature and suggests thatadditional research in this endeavor will likely produce even better tools that will help researchers cope with vast amounts of knowledge being produced.

【 授权许可】

CC BY