About: Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets
Creator	Moghaddam, & Ramanna,
Source	Elsevier; Medline; PMC
abstract	Construction of knowledge repositories from web corpora by harvesting linguistic patterns is of benefit for many natural language-processing applications that rely on question-answering schemes. These methods require minimal or no human intervention and can recursively learn new relational facts-instances in a fully automated and scalable manner. This paper explores the performance of tolerance rough set-based learner with respect to two important issues: scalability and its effect on concept drift, by (1) designing a new version of the semi-supervised tolerance rough set-based pattern learner (TPL 2.0), (2) adapting a tolerance form of rough set methodology to categorize linguistic patterns, and (3) extracting categorical information from a large noisy dataset of crawled web pages. This work demonstrates that the TPL 2.0 learner is promising in terms of precision@30 metric when compared with three benchmark algorithms: Tolerant Pattern Learner 1.0, Fuzzy-Rough Set Pattern Learner, and Coupled Bayesian Sets-based learner.
has issue date	2020-06-26(xsd:dateTime)
bibo:doi	10.1016/j.patter.2020.100053
bibo:pmid	32835308
has license	no-cc
sha1sum (hex)	c1e5b937c6c0b09afe12ace09a07c53ff4ab39c4
schema:url	https://doi.org/10.1016/j.patter.2020.100053
resource representing a document's title	Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets
has PubMed Central identifier	PMC7318947
has PubMed identifier	32835308
schema:publication	Patterns (N Y)
resource representing a document's body	covid:c1e5b937c6c0b09afe12ace09a07c53ff4ab39c4#body_text
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