Extracting Core Elements of TFM Functional Characteristics from Stanford CoreNLP Application Outcomes

ENASE 2019: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering 2019
Ērika Nazaruka, Jānis Osis, Viktorija Gribermane

Stanford CoreNLP is the Natural Language Processing (NLP) pipeline that allow analysing text at paragraph, sentence and word levels. Its outcomes can be used for extracting core elements of functional characteristics of the Topological Functioning Model (TFM). The TFM elements form the core of the knowledge model kept in the knowledge base. The knowledge model ought to be the core source for further model transformations up to source code. This paper presents research on main steps of processing Stanford CoreNLP application results to extract actions, objects, results and executors of the functional characteristics. The obtained results illustrate that such processing can be useful, however, requires text with rigour, and even uniform, structure of sentences as well as attention to the possible parsing errors.

Keywords
Knowledge Acquisition, Natural Language Processing, Stanford Corenlp, Functional Feature, Topological Functioning Model, Computation Independent Model
DOI
10.5220/0007831605910602
Hyperlink
https://www.scitepress.org/Link.aspx?doi=10.5220/0007831605910602

Nazaruka, Ē., Osis, J., Gribermane, V. Extracting Core Elements of TFM Functional Characteristics from Stanford CoreNLP Application Outcomes. In: ENASE 2019: Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering, Greece, Heraklion, Crete, 4-5 May, 2019. [S.l.]: SciTePress, 2019, pp.591-602. ISBN 978-989-758-375-9. Available from: doi:10.5220/0007831605910602

Publication language
English (en)