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An approach to the problem of annotation of research publications

Ch. 58. P. 429–434.
Artemova E.

An approach to multiple labeling research papers is explored. We develop techniques for annotating/labeling research pa- pers 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 anno- tation. Three phrase-to-text relevance measures are experi- mentally compared in this setting. The measures are: (a) co- sine relevance score between conventional vector space repre- sentations of the texts coded with tf-idf weighting; (b) pop- ular 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. 

 

Language: English
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Keywords: annotated suffix treephrase-to-text relevancetext labeling
Publication based on the results of:
Mathematical models, algorithms and software for data mining in the text and the structural form (2014)

In book

Proceedings of The Eighth International Conference on Web Search and Data Mining
NY, United States of America: ACM, 2014.
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