Abstract
Knowledge extraction from scientific documents plays an important role in the development of academic databases and services. We focus on the processing of abstracts to academic papers for the purposes of research data structuring that includes various subtasks, such as key phrase extraction and clustering. The use of abstracts is beneficial, because authors keep up with formal and stylistic requirements imposed by the publishers, and, therefore, informational and language patterns can be revealed. From our viewpoint, the existence of these patterns makes it possible to perform the cross-task application of techniques used for abstracts processing. The aim of the paper is to show it.
The reported study was funded by RFBR according to the research project No. 16-37-00430 mol_a and partially supported by the Government of Russian Federation, Grant 074-U01.
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Notes
- 1.
Weka package: http://www.cs.waikato.ac.nz/ml/weka/.
- 2.
Pre-processed collections: http://sites.google.com/site/merrecalde/resources.
- 3.
Stanford PoS tagging tool: http://nlp.stanford.edu/software/tagger.html.
- 4.
Porter Stemmer: http://tartarus.org/martin/PorterStemmer.
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Popova, S., Danilova, V. (2017). Using Extended Stopwords Lists to Improve the Quality of Academic Abstracts Clustering. In: Ciuciu, I., et al. On the Move to Meaningful Internet Systems: OTM 2016 Workshops. OTM 2016. Lecture Notes in Computer Science(), vol 10034. Springer, Cham. https://doi.org/10.1007/978-3-319-55961-2_24
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DOI: https://doi.org/10.1007/978-3-319-55961-2_24
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