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2020 | OriginalPaper | Chapter

Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions

Authors : Erika Nazaruka, Jānis Osis, Viktorija Griberman

Published in: Evaluation of Novel Approaches to Software Engineering

Publisher: Springer International Publishing

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Abstract

Automated extraction of semantic information from textual descriptions can be implemented by processing results of application of Stanford CoreNLP tools. This paper presents a sequence of processing steps and initial results of their application for two examples of a description of system’s functionality. The processing steps allow identifying main functional characteristics of the system and its operational domain. Results obtained as a result of application of the steps are compared with data obtained as a result of analysis by a developer. Application of Stanford CoreNLP parsers in certain cases can produce errors and can influence results of further processing. The comparison of the two results sets showed that variability of language constructs in descriptions affects an amount of implicitly expressed knowledge. Nevertheless, results of this research can be used as a start point of automated text processing for creation of analysis models.

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next chapter An Overview of Ways of Discovering Cause-Effect Relations in Text by Using Natural Language Processing
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Metadata
Title
Using Stanford CoreNLP Capabilities for Semantic Information Extraction from Textual Descriptions
Authors
Erika Nazaruka
Jānis Osis
Viktorija Griberman
Copyright Year
2020
Book
Evaluation of Novel Approaches to Software Engineering

Print ISBN: 978-3-030-40222-8

Electronic ISBN: 978-3-030-40223-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-40223-5_1

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