Paper Info
Title
Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies.
Abstract
In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. [12] has showed that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for seeded classes. The Exploratory learning [12] method was proposed to solve this problem; however it is limited to the flat classification task. This paper builds such exploratory learning methods for hierarchical classification tasks. We experimented with subsets of the NELL [8] ontology and text, and HTML table datasets derived from the ClueWeb09 corpus. Our method (OptDAC-ExploreEM) outperforms the existing Exploratory EM method, and its naive extension (DAC-ExploreEM), in terms of seed class F1 on average by 10% and 7% respectively.
Year
DOI
Venue
2016
10.1145/2835776.2835810
WSDM
Keywords
Field
DocType
Semi-supervised Learning, Hierarchical Classification, Ontology Extension, Concept Discovery
Data mining,Ontology,Semi-supervised learning,One-class classification,Computer science,Exploratory learning,Artificial intelligence,Ontology extension,Hierarchy,Machine learning,Semantic change
Conference
Citations 
PageRank 
References 
7
0.67
15
Authors
3
Name
Order
Citations
PageRank
Bhavana Bharat Dalvi120117.31
A. Mishra2345.00
William W. Cohen3101781243.74