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In this work, two simple methods of tagging\udscientific publications with labels reflecting their content\udare presented and compared. As a first source of labels,\udWikipedia is employed. A second label set is constructed\udfrom the noun phrases occurring in the analyzed corpus. The\udcorpus itself consists of abstracts from 0.7 million scien-\udtific documents deposited in the ArXiv preprint collection.\udWe present a comparison of both approaches, which shows\udthat discussed methods are to a large extent complemen-\udtary. Moreover, the results give interesting insights into the\udcompleteness of Wikipedia knowledge in various scientific\uddomains. As a next step, we examine the statistical proper-\udties of the obtained tags. It turns out that both methods show\udqualitatively similar rank–frequency dependence, which is\udbest approximated by the stretched exponential curve. The\uddistribution of the number of distinct tags per document fol-\udlows also the same distribution for both methods and is well\uddescribed by the negative binomial distribution. The devel-\udoped tags are meant for use as features in various text mining\udtasks. Therefore, as a final step we show the preliminary\udresults on their application to topic modeling.Michał Łopuszyńsk

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