loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Erika Nazaruka

Affiliation: Department of Applied Computer Science, Riga Technical University, Sētas iela 1, Riga and Latvia

Keyword(s): System Modelling, Knowledge Extraction, Natural Language Processing, Topological Functioning Model.

Abstract: Identification of cause-effect relations in the domain is crucial for construction of its correct model, and especially for the Topological Functioning Model (TFM). Key elements of the TFM are functional characteristics of the system and cause-effect relations between them. Natural Language Processing (NLP) can help in automatic processing of textual descriptions of functionality of the domain. The current research illustrates results of a survey of research papers on identification and extracting cause-effect relations from text using NLP and other techniques. The survey shows that expression of cause-effect relations in text can be very different. Sometimes the same language constructs indicate both causal and non-causal relations. Hybrid solutions that use machine learning, ontologies, linguistic and syntactic patterns as well as temporal reasoning show better results in extracting and filtering cause-effect pairs. Multi cause and multi effect domains still are not very well studi ed. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 216.73.216.12

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Nazaruka and E. (2019). Identification of Causal Dependencies by using Natural Language Processing: A Survey. In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - MDI4SE; ISBN 978-989-758-375-9; ISSN 2184-4895, SciTePress, pages 603-613. DOI: 10.5220/0007842706030613

@conference{mdi4se19,
author={Erika Nazaruka},
title={Identification of Causal Dependencies by using Natural Language Processing: A Survey},
booktitle={Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - MDI4SE},
year={2019},
pages={603-613},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007842706030613},
isbn={978-989-758-375-9},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - MDI4SE
TI - Identification of Causal Dependencies by using Natural Language Processing: A Survey
SN - 978-989-758-375-9
IS - 2184-4895
AU - Nazaruka, E.
PY - 2019
SP - 603
EP - 613
DO - 10.5220/0007842706030613
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic