Publikasjoner
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Holter, Ole Magnus & Ell, Basil
(2021).
Towards Scope Detection in Textual Requirements.
I Gromann, Dagmar; Gilles, Sérasset; Declerck, Thierry; McCrae, John P.; Gracia, Jorge; Bosque-Gil, Julia; Bobillo, Fernando & Heinisch, Barbara (Red.),
3rd Conference on Language, Data and Knowledge (LDK 2021).
Schloss Dagstuhl-Leibniz-Zentrum für Informatik..
ISSN 978-3-95977-199-3.
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Holter, Ole Magnus
(2020).
Semantic Parsing of Textual Requirements.
I Harth, Andreas; Presutti, Valentina; Troncy, Raphaël; Acosta, Maribel; Polleres, Axel; Fernández, Javier D.; Xavier Parreira, Josiane; Hartig, Olaf; Hose, Katja & Cochez, Michael (Red.),
The Semantic Web: ESWC 2020 Satellite Events.
Springer.
ISSN 978-3-030-62326-5.
s. 240–249.
doi:
10.1007/978-3-030-62327-2_39.
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Holter, Ole Magnus; Myklebust, Erik B; Chen, Jiaoyan & Jimenez-Ruiz, Ernesto
(2019).
Embedding OWL ontologies with OWL2Vec.
CEUR Workshop Proceedings.
ISSN 1613-0073.
2456,
s. 33–36.
Fulltekst i vitenarkiv
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In this paper, we present a preliminary study to compute embeddings
for OWL 2 ontologies by projecting the ontology axioms into a graph and performing
(random) walks over the ontology graph to create a corpus of sentences.
This corpus is then given to a neural language model to create concept embeddings.
The conducted preliminary evaluation shows promising results.
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Holter, Ole Magnus & Ell, Basil
(2023).
Reading Between the Lines: Information Extraction from Industry Requirements.
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Industry requirements describe the qualities that a project or a service must provide. Most requirements are, however, only available in natural language format and are embedded in textual documents. To be machine-understandable, a requirement needs to be represented in a logical format. We consider that a requirement consists of a scope, which is the requirement's subject matter, a condition, which is any condition that must be fulfilled for the requirement to be relevant, and a demand, which is what is required.
We introduce a novel task, the identification of the semantic components scope, condition, and demand in a requirement sentence, and establish baselines using
sequence labelling and few-shot learning. One major challenge with this task is the implicit nature of the scope, often not stated in the sentence. By including document context information, we improved the average performance for scope detection. Our study provides insights into the difficulty of machine understanding of industry requirements and suggests strategies for addressing this challenge.
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Holter, Ole Magnus & Ell, Basil
(2023).
Human-Machine Collaborative Annotation: A Case Study with GPT-3.
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Within industry, it is vital to adequately communicate the qualities and features of what is to be built, and requirements are important artefacts for this purpose. Having machine-readable requirements can enhance the level of control over the requirements, allowing more efficient requirement management and communication.
Training a semantic parser typically requires a dataset with thousands of examples. However, creating such a dataset for textual requirements poses significant challenges. In this study, we investigate to what extent a large language model can assist a human annotator in creating a gold corpus for semantic parsing of textual requirements.
The language model generates a semantic parse of a textual requirement that is then corrected by a human and then added to the gold standard. Instead of incrementally fine-tuning the language model on the growing gold standard, we investigate different strategies of including examples from the growing gold standard in the prompt for the language model.
We found that selecting the requirements most semantically similar to the target sentence and ordering them with the most similar requirement first yielded the best performance on all the metrics we used. The approach resulted in 41 % fewer edits compared to creating the parses from scratch, - thus, significantly less human effort is involved in the creation of the gold standard in collaborative annotation. Our findings indicate that having more requirements in the gold standard improves the accuracy of the initial parses.
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Publisert
27. nov. 2019 11:25
- Sist endret
22. nov. 2021 10:11