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Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies
This paper builds such exploratory learning methods for hierarchical classification tasks with subsets of the NELL ontology and text, and HTML table datasets derived from the ClueWeb09 corpus, and outperforms the existing Exploratory EM method, and its naive extension, in terms of seed class F1 on average by 10% and 7% respectively.
SENTIMENT ANALYSIS OF ONLINE USER REVIEWS
The project aims to improve the existing methods that are being employed in the field of sentiment analysis of online user reviews. The proposed method is a dual training algorithm that analyses both…
A SUPERVISED LEARNING METHOD TO CLUSTER XML DOCUMENTS WITH REDUCED COMPLEXITY
This paper presents an efficient methodology for clustering of XML documents that considers both structure and content of documents and finds out the cluster to which the document belongs to.