Towards robust tags for scientific publications from natural language processing tools and Wikipedia

Abstract

In this work, two simple methods of tagging scientific publications with labels reflecting their content are presented and compared. As a first source of labels, Wikipedia is employed. A second label set is constructed from the noun phrases occurring in the analyzed corpus. The corpus itself consists of abstracts from 0.7 million scien- tific documents deposited in the ArXiv preprint collection. We present a comparison of both approaches, which shows that discussed methods are to a large extent complemen- tary. Moreover, the results give interesting insights into the completeness of Wikipedia knowledge in various scientific domains. As a next step, we examine the statistical proper- ties of the obtained tags. It turns out that both methods show qualitatively similar rank–frequency dependence, which is best approximated by the stretched exponential curve. The distribution of the number of distinct tags per document fol- lows also the same distribution for both methods and is well described by the negative binomial distribution. The devel- oped tags are meant for use as features in various text mining tasks. Therefore, as a final step we show the preliminary results on their application to topic modeling.