Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
On the merits of building categorization systems by supervised clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An investigation of linguistic features and clustering algorithms for topical document clustering
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Concept decompositions for large sparse text data using clustering
Machine Learning
The VLDB Journal — The International Journal on Very Large Data Bases
TopCat: Data Mining for Topic Identification in a Text Corpus
IEEE Transactions on Knowledge and Data Engineering
Taxonomies by the numbers: building high-performance taxonomies
Proceedings of the 14th ACM international conference on Information and knowledge management
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The cluster-abstraction model: unsupervised learning of topic hierarchies from text data
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Navigating within news collections using tag-flakes
Journal of Visual Languages and Computing
Semi-Automatic Ontology Construction by Exploiting Functional Dependencies and Association Rules
International Journal on Semantic Web & Information Systems
Discovering generalized association rules from Twitter
Intelligent Data Analysis
| Hi-index | 0.00 |
In this paper we investigate the possibility of an automatic construction of conceptual taxonomies and evaluate the achievable results. The hierarchy is performed by Ward algorithm, guided by Goodman-Kruskal 茂戮驴as proximity measure. Then, we provide a concise description of each cluster by a keyword representative selected by PageRank.The obtained hierarchy has the same advantages - both descriptive and operative - of indices on keywords which partition a set of documents with respect to their content.We performed experiments in a real case - the abstracts of the papers published in ACM TODS in which the papers have been manually classified into the ACM Computing Taxonomy (CT). We evaluated objectively the generated hierarchy by two methods: Jaccard measure and entropy. We obtained good results by both the methods. Finally we evaluated the capability to classify in the categories of the two taxonomies showing that KH provides a greater facility than CT.