Relying on Discourse Trees to Extract Medical Ontologies from Text

Galitsky, Boris; Ilvovsky, Dmitry; Goncharova, Elizaveta

doi:10.1007/978-3-030-86855-0_15

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The significance of using contexts is shown by some refined argument in [5]. Also, we can note that the idea of using decision trees (classification structure) for an organization principle of mining ontology from texts is proposed in [9].…”

Section: Related Workmentioning

confidence: 99%

Plausible Reasoning in an Algorithm for Generation of Good Classification Tests

Naidenova,

Parkhomenko,

Martirova

et al. 2024

ARC

View full text Add to dashboard Cite

The paper is devoted to the application of the plausible reasoning principles to symbolic machine learning. It seems for us that the applications are essential and necessary to improve the efficiency of ML algorithms. Many such algorithms produce and use rules in the form of implication. The generation of these rules with respect to the object classes is discussed. Our classification rules are specific. Their premise part, called good closed tests (GCTs), should cover as many objects as possible. One of the algorithms of GCTs generation called NIAGARA is presented. The algorithm is revisited and new procedures based on plausible reasoning are proposed. Their correctness is proved in propositions. We use the following rules: implication, interdiction, inductive rules of extending current sets of goal-oriented objects, rules of pruning the domain of searching solution. They allow to rise the effectiveness of algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%