Knowledge discovery in inspection reports of marine structures

Lee, Seung-Kyung; Kim, Bong-Seok; Huh, Minhoe; Park, Jooseoung; Kang, Seokho; Cho, Sungzoon; Lee, Dongha; Lee, Daehyung

doi:10.1016/j.eswa.2013.07.109

Cited by 9 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We find that the algorithm achieves clustering performance comparable to that reported by the best of the comparable work in the field . Recent examples of work similar to ours applied to a variety of texts such as marine inspection reports, crime reports, and qualitative research data are found in Refs . Unfortunately, these papers do not report their results in as much detail as we do, and thus we have been unable to make a quantitative comparison between these papers and our work.…”

Section: Related Worksupporting

confidence: 52%

Self‐organizing maps for latent semantic analysis of free‐form text in support of public policy analysis

Till

Longo

Dobell

et al. 2013

WIREs Data Min & Knowl

View full text Add to dashboard Cite

The huge amount of free-form unstructured text in the blogosphere, its increasing rate of production, and its shrinking window of relevance, present serious challenges to the public policy analyst who seeks to take public opinion into account. Most of the tools which address this problem use XML tagging and other Web 3.0 approaches, which do not address the actual content of blog posts and the associated commentary. We give a tutorial review of latent semantic analysis and the self-organizing maps, as considered in this context, and show how to apply the self-organizing map over a probabilistic latent semantic space to the problem of completely unsupervised clustering of unstructured text in such a way as to be entirely independent of spelling, grammar, and even source language. This provides an algorithm suitable for clustering free-form commentary with a well-structured test environment. The algorithm is applied to academic paper abstracts instead, treated as unstructured text as though they were blog posts, because this set of documents has a known ground truth. The algorithm constructs a word category map and a document map in which words with similar meaning and documents with similar content are clustered together.

show abstract