2003 WordnetImprovesTextDocumentClustering
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- (Hotho et al., 2003) ⇒ Andreas Hotho, Steffen Staab, Gerd Stumme. (2003). “Wordnet Improves Text Document Clustering.” In: Proceedings of the SIGIR Workshop on Semantic Web Workshop.
Subject Headings: Text Clustering Algorithm, WordNet, Bisecting k-Means Clustering Algorithm.
Notes
- It proposes a Text Clustering Algorithm.
- It analyzes the benefits of using WordNet Synsets and up to five levels of Hypernyms
- It uses the Bisecting k-Means Algorithm.
- It analyzes the addition of Part of Speech tags
- It analyzes WSD by context which returns the concept which maximizes a function depending on the conceptual vicinity. Given a concept c, its semantic vicinity is defined as the set of all its direct sub and super concepts.
- It relates to their prior work: (Hotho et al., 2001).
Cited By
2007
- (Recupero, 2007) ⇒ Diego Reforgiato Recupero. (2007). “A New Unsupervised Method for Document Clustering by using WordNet Lexical and Conceptual Relations.” In: Information Retrieval (2007) 10:563–579.
2004
- (Sedding and Kazakov, 2004) ⇒ Julian Sedding and Dimitar Kazakov. (2004). “Wordnet-based Text Document Clustering..” In: COLING-2004 Workshop on Robust Methods in Analysis of Natural Language Data (ROMAND).
Quotes
Abstract
Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the problem, we integrate background knowledge — in our application Wordnet — into the process of clustering text documents. We cluster the documents by a standard partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks.
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