An approach to multiple labelling research papers is explored. We develop techniques for annotating/labeling research papers in informatics and computer sciences with key phrases taken from the ACM Computing Classification System. The techniques utilize a phrase-to-text relevance measure so that only those phrases that are most relevant go to the annotation. Three phrase-to-text relevance measures are experimentally compared in this setting. The measures are: (a) cosine relevance score between conventional vector space representations of the texts coded with tf-idf weighting; (b) popular characteristic of probability of term generation BM25; and (c) an in-house characteristic of conditional probability of symbols averaged over matching fragments in suffix trees representing texts and phrases, CPAMF. In an experiment conducted over a set of texts published in journals of the ACM and manually annotated by their authors, CPAMF outperforms both the cosine measure and BM25 by a wide margin.